*Article* **Methodology for the Assessment of Multi-Hazard Risk in Urban Homogenous Zones**

**Nenad Mladineo 1 , Marko Mladineo 2 , Elena Benvenuti 3 , Toni Kekez 1 and Željana Nikoli´c 1,\***

**Abstract:** The multi-hazard risk assessment of urban areas represents a comprehensive approach that can be used to reduce, manage and overcome the risks arising from the combination of different natural hazards. This paper presents a methodology for multi-hazard risk assessment based on Spatial Multi-Criteria Decision Making. The PROMETHEE method was used to assess multi-hazard risks caused by seismic, flood and extreme sea waves impact. The methodology is applied for multihazard risk evaluation of the urban area of Kaštel Kambelovac, located on the Croatian coast of the Adriatic Sea. The settlement is placed in a zone of high seismic risk with a large number of old stone historical buildings which are vulnerable to the earthquakes. Being located along the low-lying coast, this area is also threatened by floods due to climate change-induced sea level rises. Furthermore, the settlement is exposed to flooding caused by extreme sea waves generated by severe wind. In the present contribution, the multi-hazard risk is assessed for different scenarios and different levels, based on exposure and vulnerability for each of the natural hazards and the influence of additional criteria to the overall risk in homogenous zones. Single-risk analysis has shown that the seismic risk is dominant for the whole pilot area. The results of multi-hazard assessment have shown that in all combinations the highest risk is present in the historical part of Kaštel Kambelovac. This is because the historical part is most exposed to sea floods and extreme waves, as well as due to the fact that a significant number of historical buildings is located in this area.

**Keywords:** risk assessment; multi hazard; multi-hazard risk assessment; multi-criteria decisionmaking; GIS; PROMETHEE method

**1. Introduction**

Natural hazards are threatening the population throughout the world more than ever. Efficient planning and preparation are vital, since the question "will the disaster happen?", has changed into "when will it happen?". Enhancing the safety and resilience for disasters requires knowledge about individual territorial hazards, vulnerabilities and risks. Appropriate multi-risk methodology based on existing data and knowledge should produce an interactive and easily understanding map that will enable the visualization of individual and combined risks. Integration of this methodology into the Geographic Information System gives important information to local and regional authorities for preventing, managing and overcoming multi-hazard natural disasters [1], such as river and sea floods, meteotsunamis (or extreme sea waves) and earthquakes. To reduce the possible loss of life and damage to property caused by hazards [2], it is crucial to conduct risk assessments and make decisions pertaining to natural hazards before the hazards occur [3].

A common practice in the hazard risk assessment is to focus on the hazard frequency and intensity in combination with area vulnerability or severity of damage caused by the hazard [4]. Furthermore, the severity is not just the result of hazard intensity and area

**Citation:** Mladineo, N.; Mladineo, M.; Benvenuti, E.; Kekez, T.; Nikoli´c, Ž. Methodology for the Assessment of Multi-Hazard Risk in Urban Homogenous Zones. *Appl. Sci.* **2022**, *12*, 12843. https://doi.org/10.3390/ app122412843

Academic Editor: Jianbo Gao

Received: 27 October 2022 Accepted: 9 December 2022 Published: 14 December 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

vulnerability, but it is also influenced by the coping capacity of the emergency units in the area [5]. Hazard occurs in some periods with particular intensity and causes damage in relation to area vulnerability and coping capacity. A brief literature survey was made among scientific and professional papers to investigate what are the common factors used to calculate the risk of natural hazards (Table 1).

**Table 1.** Brief literature survey on factors used to calculate the risk of natural hazards.

Table 1 shows that hazard frequency (or probability of occurrence) is the most common factor used in risk assessment. Some approaches are focused on hazard intensity and some of them on vulnerability in combination with damage or loss. However, half of the papers dealing with multi-hazard approach are taking into account all three emphasized factors—frequency, intensity and vulnerability—including other factors. In the context of the multi-hazard risk assessment, many factors are used. Since each factor can represent one criterion, evaluation of these factors can be used as an input matrix for the Multi-Criteria Analysis (MCA). The reason for using so many criteria (factors) for multi-hazard risk assessments is due to its complexity. According to the standard for risk assessment IEC 31010:2009 [14], the risk assessment is the overall following process: risk identification, risk analysis and risk evaluation, and it is recommended to use multi-criteria analysis or multi-criteria decision-analysis method for the risk assessment.

The problem of the multi-hazard risk assessment is not just in designing a proper calculation to aggregate all hazard risks in one area [15,16], but also to take into account hazards' mutual correlation [9], since one hazard can trigger another one. For instance, fire is usually spread after earthquakes, and earthquakes can produce tsunamis, thus flooding the area.

Although the world is dealing with hazards that have an increasing frequency, like flood disasters that are caused by extreme climate and urbanization processes [17], another problem of the single-hazard or multi-hazard risk assessment lies in a specific type of hazards. There are specific hazards, earthquakes, for instance, that can be represented as low-probability/high-consequence events [10]. This issue represents a large problem, as earthquakes can have high intensity while their frequency is usually very low, so the risk calculation of multiplying intensity and frequency will result in a low risk level. Therefore, additional factors need to be taken into calculation to emphasize the risk from earthquakes and similar hazards that are low-probability/high-consequence events.

Vulnerability is one of the most important factors in the risk assessment [18]. Namely, high vulnerability of some areas can result in severe losses during a low-intensity hazard, and low vulnerability can result in minor losses during a high-intensity hazard. Many different criteria are used for vulnerability calculation, because the criteria set is also defined by the type of hazard [4]. However, assessing the vulnerability to natural hazards such as earthquakes can be characterized as an ill-structured problem or a problem without unique, identifiable and objectively optimal solution. A review of the literature indicates a number of contrasting definitions of what vulnerability means, as well as numerous conflicting perspectives on what should or should not be included within the broad assessment of vulnerability in cities [19]. For instance, some authors also include coping capacity (emergency units) of the area in the vulnerability analysis [5]. But this is not necessarily a good approach, since coping capacity is something that can dynamically change each year, and other common vulnerability criteria (building age, building structure, building height, etc.) are less or more static. Furthermore, it is important to mention that different vulnerability analyses are used at different scales [20]. The different criteria sets (factors) are used and sometimes different methods must be used, as well. without unique, identifiable and objectively optimal solution. A review of the literature indicates a number of contrasting definitions of what vulnerability means, as well as numerous conflicting perspectives on what should or should not be included within the broad assessment of vulnerability in cities [19]. For instance, some authors also include coping capacity (emergency units) of the area in the vulnerability analysis [5]. But this is not necessarily a good approach, since coping capacity is something that can dynamically change each year, and other common vulnerability criteria (building age, building structure, building height, etc.) are less or more static. Furthermore, it is important to mention that different vulnerability analyses are used at different scales [20]. The different criteria sets (factors) are used and sometimes different methods must be used, as well.

defined by the type of hazard [4]. However, assessing the vulnerability to natural hazards such as earthquakes can be characterized as an ill-structured problem or a problem

The aim of this study is the assessment of multi-hazard risk for Kaštel Kambelovac, a small city placed along the Adriatic Sea near the city of Split, Croatia (Figure 1). The aim of this study is the assessment of multi-hazard risk for Kaštel Kambelovac, a small city placed along the Adriatic Sea near the city of Split, Croatia (Figure 1).

*Appl. Sci.* **2022**, *12*, x FOR PEER REVIEW 3 of 23

**Figure 1.** The City of Kaštela: (**a**) Historical center of Kaštel Kambelovac [21]; (**b**) Coastal flooding events in the City of Kaštela [22,23]. **Figure 1.** The City of Kaštela: (**a**) Historical center of Kaštel Kambelovac [21]; (**b**) Coastal flooding events in the City of Kaštela [22,23].

Due to its location in a zone of high seismic risk and considering the large number of stone-made and several-centuries-old historical buildings, Kaštela is a settlement with pronounced seismic vulnerability and risk [18]. The historical center, built right next to the low-lying coast, is also threatened by floods due to rising sea levels caused by climate change. Furthermore, the settlement is exposed to flooding caused by extreme sea waves generated by severe wind. Therefore, the present multi-hazard risk assessment is aimed at determining the combined risk of the settlement caused by earthquakes, sea floods and extreme sea waves. The multi-hazard risk investigation of the Kaštel Kambelovac is a part of the project "Preventing, managing, and overcoming natural-hazards risks to mitigate economic and social impact" (PMO-GATE) [24]. Due to its location in a zone of high seismic risk and considering the large number of stone-made and several-centuries-old historical buildings, Kaštela is a settlement with pronounced seismic vulnerability and risk [18]. The historical center, built right next to the low-lying coast, is also threatened by floods due to rising sea levels caused by climate change. Furthermore, the settlement is exposed to flooding caused by extreme sea waves generated by severe wind. Therefore, the present multi-hazard risk assessment is aimed atdetermining the combined risk of the settlement caused by earthquakes, sea floods andextreme sea waves. The multi-hazard risk investigation of the Kaštel Kambelovac is a partof the project "Preventing, managing, and overcoming natural-hazards risks to mitigateeconomic and social impact" (PMO-GATE) [24].

The main challenge in the multi-hazard risk assessment is to evaluate, use and mutually compare different mathematical variables that describe the hazard's frequency, intensity and vulnerability. In this research, this issue is addressed by using the multicriteria analysis that is commonly used to evaluate and compare quantitative and qualitative criteria in completely different units and the order of magnitude. However, a proper multi-criteria analysis must be selected. An additional challenge in the multihazard risk assessment is the data collection and evaluation, which can become complex on the settlement or regional level. Therefore, a proper spatial analysis must be used to organize and aggregate spatial thematic layers. Accordingly, the Geographic Information System is used in combination with multi-criteria analysis in order to establish a Spatial Multi-Criteria Decision Making system. The main challenge in the multi-hazard risk assessment is to evaluate, use and mutually compare different mathematical variables that describe the hazard's frequency, intensity and vulnerability. In this research, this issue is addressed by using the multi-criteria analysis that is commonly used to evaluate and compare quantitative and qualitative criteria in completely different units and the order of magnitude. However, a proper multi-criteria analysis must be selected. An additional challenge in the multi-hazard risk assessment is the data collection and evaluation, which can become complex on the settlement or regional level. Therefore, a proper spatial analysis must be used to organize and aggregate spatial thematic layers. Accordingly, the Geographic Information System is used in combination with multi-criteria analysis in order to establish a Spatial Multi-Criteria Decision Making system.

### **2. Methodology**

The methodology used in this research relies on the combination of the Multi-Criteria Analysis/Multi-Criteria Decision-Making (MCA/MCDM) and Geographic Information System (GIS) to evaluate and visualize this risk assessment in this particular area. The advantage of GIS is in its ability to visualize spatial data (Figure 2) and enhance the spatial decision-making in the risk assessment [25–27].

**2. Methodology**

decision-making in the risk assessment [25–27].

**Figure 2.** An example of multiple-risk map for the Ferrara province [15]. **Figure 2.** An example of multiple-risk map for the Ferrara province [15].

Furthermore, GIS supports the usage of the multi-scale approach and different criteria sets: the vulnerability analysis can be made for each building, the whole settlement, the settlement's municipality, and for the whole region. The multi-scale approach was discussed in the already-mentioned paper by Vicente et al. [20], but greater contribution was given by Aubrecht et al. [28] that presented multi-level geospatial modeling of vulnerability indicators from building level to country level. Another example of a multi-level and multi-criteria approach is presented in Figure 3. Furthermore, GIS supports the usage of the multi-scale approach and different criteria sets: the vulnerability analysis can be made for each building, the whole settlement, the settlement's municipality, and for the whole region. The multi-scale approach was discussed in the already-mentioned paper by Vicente et al. [20], but greater contribution was given by Aubrecht et al. [28] that presented multi-level geospatial modeling of vulnerability indicators from building level to country level. Another example of a multi-level and multi-criteria approach is presented in Figure 3. *Appl. Sci.* **2022**, *12*, x FOR PEER REVIEW 5 of 23

The methodology used in this research relies on the combination of the Multi-Criteria Analysis/Multi-Criteria Decision-Making (MCA/MCDM) and Geographic Information System (GIS) to evaluate and visualize this risk assessment in this particular area. The advantage of GIS is in its ability to visualize spatial data (Figure 2) and enhance the spatial

**Figure 3.** Vulnerability analysis at different scales with multi-criteria approach. **Figure 3.** Vulnerability analysis at different scales with multi-criteria approach.

The third approach is to define assessment areas as "homogeneous zones", which are generated by intersecting relevant thematic layers in the assessment area. The intersection of the defined number of layers becomes an assessment area (zone). This approach has Regarding the risk assessment, it is usually made for a particular assessment area. These areas need to be defined by mutual spatial characteristics or by some already definedurban entity.

been used in this research for Croatian settlement Kaštel Kambelovac, a part of Kaštela

In the analysis of natural hazards, impacts are often expressed in terms of hazard, vulnerability and exposure. A hazard (H) presents the probability that a harmful event will appear in a particular area and in a certain time interval. Vulnerability (V) is defined as the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard. Exposure (E) is the totality of people, property, systems or other elements present in hazard zones that are thereby subject to

In this research, the hazard (H) is presented by seismic hazard maps with a Peak Ground Acceleration—PGA for the earthquake, while, in the case of sea floods and extreme sea waves, it is based on a flood depth and a wave height in inundation areas, respectively. The exposure (E) that corresponds to the measure of hazard will be presented by intensity and Area Impact (e.g., area exposed to earthquake, flood or extreme waves). Furthermore, vulnerability is evaluated by vulnerability index on multiple levels (for the particular building, for the settlement, etc.) with the use of additional criteria set which is different for each level. Therefore, in this research, function f represents mathematical PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluations) method [29] that will connect all criteria and assess the risk for

Obviously, there are many other mathematical methods for multi-criteria analysis and decision-making, but some of them are better accepted and more widely used. Three of them have recently become the most popular: AHP [25], TOPSIS [26] and PROMETHEE [30,31]. There is also a need to decide which of the available methods is the most adequate for a particular problem, but very often, the outranking methods like PROMETHEE are the most suitable choice [32]. This is especially because PROMETHEE method can be

Furthermore, using the concept of vulnerability makes it more explicit that the impacts of a hazard are also a function of the preventive and preparatory measures that are employed to reduce the risk. Depending on the particular risk analyzed, the measurement

City.

potential losses.

the observed area.

simplified to be used by non-expert users [33,34].

The first and simplest approach is to use some administrative areas as assessment areas: settlements, municipalities, provinces, counties, etc. This approach was used in the combined seismic and flood risk assessment for municipalities in the province of Ferrara [15].

The second approach is the definition of assessment areas as "working units" by using a grid of blocks. The working unit is the geographical entity in which the calculations will be computed, hereby controlling the geographical resolution of the study. The definition of the working unit depends strongly on two factors: the geographical unit in which the original data is expressed and the scale of the study. For instance, in the urban-scale seismic risk study for city Almería, a 200 m squared grid was considered appropriate to cover the entire city of Almería, totaling an amount of 400 equal cells or working units [27].

The third approach is to define assessment areas as "homogeneous zones", which are generated by intersecting relevant thematic layers in the assessment area. The intersection of the defined number of layers becomes an assessment area (zone). This approach has been used in this research for Croatian settlement Kaštel Kambelovac, a part of Kaštela City.

### *2.1. Multi-Criteria Analysis and Decision-Making Approach to Risk Assessment*

In the analysis of natural hazards, impacts are often expressed in terms of hazard, vulnerability and exposure. A hazard (H) presents the probability that a harmful event will appear in a particular area and in a certain time interval. Vulnerability (V) is defined as the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard. Exposure (E) is the totality of people, property, systems or other elements present in hazard zones that are thereby subject to potential losses.

In this research, the hazard (H) is presented by seismic hazard maps with a Peak Ground Acceleration—PGA for the earthquake, while, in the case of sea floods and extreme sea waves, it is based on a flood depth and a wave height in inundation areas, respectively. The exposure (E) that corresponds to the measure of hazard will be presented by intensity and Area Impact (e.g., area exposed to earthquake, flood or extreme waves). Furthermore, vulnerability is evaluated by vulnerability index on multiple levels (for the particular building, for the settlement, etc.) with the use of additional criteria set which is different for each level. Therefore, in this research, function f represents mathematical PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluations) method [29] that will connect all criteria and assess the risk for the observed area.

Obviously, there are many other mathematical methods for multi-criteria analysis and decision-making, but some of them are better accepted and more widely used. Three of them have recently become the most popular: AHP [25], TOPSIS [26] and PROMETHEE [30,31]. There is also a need to decide which of the available methods is the most adequate for a particular problem, but very often, the outranking methods like PROMETHEE are the most suitable choice [32]. This is especially because PROMETHEE method can be simplified to be used by non-expert users [33,34].

Furthermore, using the concept of vulnerability makes it more explicit that the impacts of a hazard are also a function of the preventive and preparatory measures that are employed to reduce the risk. Depending on the particular risk analyzed, the measurement of risk can be carried out with a greater number of different variables and factors, depending inter alia on the complexity of the chain of impacts, the number of impact factors considered and the requisite level of precision. The scheme of assessment of single-hazard and multi-hazard exposure for the investigated coastal urban area is shown in Figure 4.

(**a**) (**b**)

**Figure 4.** The scheme of the risk assessment for investigated area: (**a**) single-hazard exposure; (**b**) multi-hazard exposure. **Figure 4.** The scheme of the risk assessment for investigated area: (**a**) single-hazard exposure; (**b**) multi-hazard exposure.

of risk can be carried out with a greater number of different variables and factors, depending inter alia on the complexity of the chain of impacts, the number of impact factors considered and the requisite level of precision. The scheme of assessment of single-hazard and multi-

hazard exposure for the investigated coastal urban area is shown in Figure 4.

#### *2.2. Risk Assessment of Buildings 2.2. Risk Assessment of Buildings*

In this research, the risk assessment is made for different levels, starting from the lowest level (micro level), i.e., an individual building. At this micro level, the risk assessment of a single-hazard exposure is based on the calculation of vulnerability indexes of buildings for individual natural disasters: In this research, the risk assessment is made for different levels, starting from the lowest level (micro level), i.e., an individual building. At this micro level, the risk assessment of a single-hazard exposure is based on the calculation of vulnerability indexes of buildings for individual natural disasters:

	- as well as on assessment of the single hazards: as well as on assessment of the single hazards:

Seismic vulnerability indexes of the buildings for investigated area are calculated according to the seismic vulnerability method [35]. The method is based on the evaluation of 11 geometrical, structural and non-structural vulnerability parameters of the building. They consider the influence of the type and quality of the structural system, the shear resistance in two horizontal directions, the position and the foundations, the properties of floors, the configuration in plan and elavation, the maximum wall spacing, the roof's typology and weight, the existence of non-structural elements, and the state of preservation. Four possibilities for each parameter were decided: from "A", indicating an optimal state, to "D", indicating a poor state. The relative importance of each parameter in the overall vulnerability is computed by using weight coefficients relating to each parameter. Finally, the vulnerability index Iv is calculated in a form I = ∑୧ s୴୧w୧, where svi is the numerical score for each class, and wi is the weight of each parameter. The vulnerability index is normalized in a 0–100% range; a low index indicates high seismic resistance and low vulnerability, while a high vulnerability index is characteristic of the buildings with low seismic resistance and high vulnerability. Vulnerability indexes of the buildings located in the pilot area are presented in Figure 5 [35]. Seismic vulnerability indexes of the buildings for investigated area are calculated according to the seismic vulnerability method [35]. The method is based on the evaluation of 11 geometrical, structural and non-structural vulnerability parameters of the building. They consider the influence of the type and quality of the structural system, the shear resistance in two horizontal directions, the position and the foundations, the properties of floors, the configuration in plan and elavation, the maximum wall spacing, the roof's typology and weight, the existence of non-structural elements, and the state of preservation. Four possibilities for each parameter were decided: from "A", indicating an optimal state, to "D", indicating a poor state. The relative importance of each parameter in the overall vulnerability is computed by using weight coefficients relating to each parameter. Finally, the vulnerability index Iv is calculated in a form IV = ∑i sviwi , where svi is the numerical score for each class, and wi is the weight of each parameter. The vulnerability index is normalized in a 0–100% range; a low index indicates high seismic resistance and low vulnerability, while a high vulnerability index is characteristic of the buildings with low seismic resistance and high vulnerability. Vulnerability indexes of the buildings located in the pilot area are presented in Figure 5 [35].

*Appl. Sci.* **2022**, *12*, x FOR PEER REVIEW 7 of 23

**Figure 5.** Seismic vulnerability index of buildings divided into 10% intervals. **Figure 5.** Seismic vulnerability index of buildings divided into 10% intervals.

The flood vulnerability index and the extreme coastal waves vulnerability index are calculated according to the methodology developed in the PMO-GATE project [25]. This methodology is based on the approach of Miranda and Ferreira [36], which takes into consideration different parameters such as building material, overall object condition, number of storeys, building age, importance of exposed objects and level of exposure. The approach has been modified for application in the multicriteria analysis, with each vulnerability index calculated as the weighted sum of set of parameters and evaluated through vulnerability classes (Figure 6). The flood vulnerability index and the extreme coastal waves vulnerability index are calculated according to the methodology developed in the PMO-GATE project [25]. This methodology is based on the approach of Miranda and Ferreira [36], which takes into consideration different parameters such as building material, overall object condition, number of storeys, building age, importance of exposed objects and level of exposure. The approach has been modified for application in the multicriteria analysis, with eachvulnerability index calculated as the weighted sum of set of parameters and evaluatedthrough vulnerability classes (Figure 6). The flood vulnerability index and the extreme coastal waves vulnerability index are calculated according to the methodology developed in the PMO-GATE project [25]. This methodology is based on the approach of Miranda and Ferreira [36], which takes into consideration different parameters such as building material, overall object condition, number of storeys, building age, importance of exposed objects and level of exposure. The approach has been modified for application in the multicriteria analysis, with each vulnerability index calculated as the weighted sum of set of parameters and evaluated through vulnerability classes (Figure 6).

**Figure 6.** Vulnerability index of buildings for: (**a**) sea floods; (**b**) extreme sea waves. **Figure 6.** Vulnerability index of buildings for: (**a**) sea floods; (**b**) extreme sea waves.

The seismic hazard for Croatia is presented with two maps for return periods of 475 and 95 years, expressed in terms of the peak ground acceleration during an earthquake for a soil class A [37]. According to HRN EN 1998-1:2011 [36], the soil types A, B, C, D and E, may be used to account for the influence of local ground conditions on the seismic action. The site can be classified according to the value of the average shear wave velocity vs,30. An investigation of the deep geology and characteristics of the terrains, performed at pilot area [38], has shown that shear wave velocity vs,30 is higher than 800 m/s at the wholearea, which define soil class A. Therefore, local ground conditions do not influence to seismic hazard in the investigated area, i.e., the seismic hazard for all buildings at the The seismic hazard for Croatia is presented with two maps for return periods of 475 and 95 years, expressed in terms of the peak ground acceleration during an earthquake for a soil class A [37]. According to HRN EN 1998-1:2011 [36], the soil types A, B, C, D and E, may be used to account for the influence of local ground conditions on the seismic action. The site can be classified according to the value of the average shear wave velocity vs,30. An investigation of the deep geology and characteristics of the terrains, performed at pilot area [38], has shown that shear wave velocity vs,30 is higher than 800 m/s at the wholearea, which define soil class A. Therefore, local ground conditions do not influence to seismic hazard in the investigated area, i.e., the seismic hazard for all buildings at the pilot area has been assumed to be constant [35].

pilot area has been assumed to be constant [35]. The flood hazard caused by climate-induced sea level rise is estimated according to IPCC Fifth Assessment Report (AR5) [39] and Strategy for climate change adaptation for The Republic of Croatia [40], considering changes in the mean sea level. However, the EU Flood Directive [41] requires an analysis of a high, moderate and low probability scenario in the flood hazard assessment. The sea level in the Adriatic Sea is dominantly caused by sea tides and the effect of the barometric pressure [42]. The tidal component can be represented by the set of periodic functions [43], and the residual sea level can be represented with a probability distribution due to its randomness and stochastic behavior. In order to fulfil the Flood Directive requirements, probability scenarios are estimated from the particular probability distribution, corresponding to the return periods of 25, 100 and 250 years, respectively [44]. Finally, sea level is estimated as a superposition of mean The flood hazard caused by climate-induced sea level rise is estimated according to IPCC Fifth Assessment Report (AR5) [39] and Strategy for climate change adaptation for The Republic of Croatia [40], considering changes in the mean sea level. However, the EU Flood Directive [41] requires an analysis of a high, moderate and low probability scenario in the flood hazard assessment. The sea level in the Adriatic Sea is dominantly caused by sea tides and the effect of the barometric pressure [42]. The tidal component can be represented by the set of periodic functions [43], and the residual sea level can be represented with a probability distribution due to its randomness and stochastic behavior. In order to fulfil the Flood Directive requirements, probability scenarios are estimated from the particular probability distribution, corresponding to the return periods of 25, 100 and 250 years, respectively [44]. Finally, sea level is estimated as a superposition of mean sea level, maximum estimated tide and each probabilistic scenario. The distribution of the critical zones most prone to flood due to the impacts of climate change on sea level rise for the most critical scenario for year 2100 is shown in Figure 7 [45].

sea level, maximum estimated tide and each probabilistic scenario. The distribution of the critical zones most prone to flood due to the impacts of climate change on sea level rise for the most critical scenario for year 2100 is shown in Figure 7 [45]. The extreme coastal waves hazard is determined based on the evaluation of wave heights and their propagation toward the coast (Figure 8). The methodology for computing the wave heights by using values of wind speeds in critical wind directions for the investigated area has been developed [46], where probability distribution function is used to fit wind speed histograms and to evaluate return period values.

*Appl. Sci.* **2022**, *12*, x FOR PEER REVIEW 9 of 23

**Figure 7.** Distribution of critical zones most prone to flood due to impact of climate changes on sea level rise: scenario for year 2100. **Figure 7.** Distribution of critical zones most prone to flood due to impact of climate changes on sea level rise: scenario for year 2100. computing the wave heights by using values of wind speeds in critical wind directions for the investigated area has been developed [46], where probability distribution function is used to fit wind speed histograms and to evaluate return period values.

**Figure 8.** Distribution of critical zones most prone to extreme sea waves exposure. **Figure 8.** Distribution of critical zones most prone to extreme sea waves exposure.

Vulnerability indexes of buildings for individual natural disasters coupled by the corresponding hazards are the basis for the multi-hazard risk assessment of the area [47]. Vulnerability indexes of buildings for individual natural disasters coupled by the corresponding hazards are the basis for the multi-hazard risk assessment of the area [47].

### *2.3. Risk Assessment of Homogenous Zones*

**Figure 8.** Distribution of critical zones most prone to extreme sea waves exposure. Vulnerability indexes of buildings for individual natural disasters coupled by the corresponding hazards are the basis for the multi-hazard risk assessment of the area [47]. At the higher level of analysis, which is the intermediate level, the previously-analyzed objects are grouped into spatial units (assessment area) that are called "homogeneous zones" [47]. The process of creation of homogenous zones for the pilot site is presented in Figure 9. In this case, three different layers are intersected: a layer of specific urban characteristics, a layer of areas surrounded by the main roads and a layer of terrain height.

*2.3. Risk Assessment of Homogenous Zones*

of terrain height.

**Figure 9.** The creation of homogenous zones: (**a**) four areas defined by specific urban characteristics; (**b**) four areas defined by main roads; (**c**) three areas defined by terrain (contours 5 and 10 m); (**d**) intersection of layers resulted with 15 homogenous zones. **Figure 9.** The creation of homogenous zones: (**a**) four areas defined by specific urban characteristics; (**b**) four areas defined by main roads; (**c**) three areas defined by terrain (contours 5 and 10 m); (**d**) intersection of layers resulted with 15 homogenous zones.

At the higher level of analysis, which is the intermediate level, the previouslyanalyzed objects are grouped into spatial units (assessment area) that are called "homogeneous zones" [47]. The process of creation of homogenous zones for the pilot site is presented in Figure 9. In this case, three different layers are intersected: a layer of specific urban characteristics, a layer of areas surrounded by the main roads and a layer

The seismic vulnerability indexes of individual buildings are used to calculate the seismic vulnerability index of a homogeneous zone. In the same way, the other vulnerabilities indexes are evaluated. However, it is important to highlight that the additional criteria are very important at this level of analysis. Since homogeneous zones have complex characteristics, additional criteria are used for both single-hazard and a multi-hazard approach, beside vulnerability and hazard. The seismic vulnerability indexes of individual buildings are used to calculate the seismic vulnerability index of a homogeneous zone. In the same way, the other vulnerabilities indexes are evaluated. However, it is important to highlight that the additional criteria are very important at this level of analysis. Since homogeneous zones have complex characteristics, additional criteria are used for both single-hazard and a multi-hazard approach, beside vulnerability and hazard.

The literature review shows that the level of risk to the community depends on a number of other parameters whose activation in a particular hazard reduces the resilience to extraordinary events. Each area due to difference in size requires a special approach in identifying the relevant parameters. For example, the most common parameters (criteria) for seismic hazard are grouped into area characteristics (geology, soil, slope, historical earthquake events, fault line, etc.), the characteristics of human intervention in space (land use, built communal infrastructure and roads, etc.), and social characteristics (housing density, social purpose of buildings, social structure, etc.). The literature review shows that the level of risk to the community depends on a number of other parameters whose activation in a particular hazard reduces the resilience to extraordinary events. Each area due to difference in size requires a special approach in identifying the relevant parameters. For example, the most common parameters (criteria) for seismic hazard are grouped into area characteristics (geology, soil, slope, historical earthquake events, fault line, etc.), the characteristics of human intervention in space (land use, built communal infrastructure and roads, etc.), and social characteristics (housing density, social purpose of buildings, social structure, etc.).

For the pilot site at the intermediate level, additional parameters that can be quantified are detected, different for each homogeneous zone. They represent additional criteria for the risk assessment of homogenous zones: communal infrastructure, road network, construction density (distance between buildings), inhabitation density, importance factor (public building, school, etc.), and historical buildings. For the pilot site at the intermediate level, additional parameters that can be quantified are detected, different for each homogeneous zone. They represent additional criteria for the risk assessment of homogenous zones: communal infrastructure, road network, construction density (distance between buildings), inhabitation density, importance factor (public building, school, etc.), and historical buildings.

### **3. Results 3. Results**

### *3.1. Single-Hazard Risk Asssessment of Homogenous Zones 3.1. Single-Hazard Risk Asssessment of Homogenous Zones*

Single-hazard risk assessment is made for 14 homogeneous zones of the pilot site. Since the flood and extreme waves are affecting only a small coastal area, the seismic risk Single-hazard risk assessment is made for 14 homogeneous zones of the pilot site. Since the flood and extreme waves are affecting only a small coastal area, the seismic risk assessment will be presented here. The input data are calculated, or expert estimates are given for the following criteria:

given for the following criteria: - Seismic hazard—PGA,

*Appl. Sci.* **2022**, *12*, x FOR PEER REVIEW 11 of 23

Since seismic hazard (PGA) and geology have the same values in each of the 14 homogeneous zones, they do not need be included in the numerical processing. The average seismic vulnerability has been calculated for each homogenous zone (Table 2). homogeneous zones, they do not need be included in the numerical processing. The average seismic vulnerability has been calculated for each homogenous zone (Table 2).

assessment will be presented here. The input data are calculated, or expert estimates are

**Table 2.** The basic data of homogenous zones and average seismic vulnerability index. **Table 2.** The basic data of homogenous zones and average seismic vulnerability index. **Homogenous Zone (HZ) Area (m2) Number of Buildings Seismic Vulnerability Index of Homogenous Zone**

The criterion inhabitation density is generated from a digitized population census. For all other additional criteria a profound GIS analysis has been made, and criteria evaluations for each homogenous zone have been calculated or estimated (Figure 10). The criterion inhabitation density is generated from a digitized population census. For all other additional criteria a profound GIS analysis has been made, and criteria evaluations for each homogenous zone have been calculated or estimated (Figure 10).

**Figure 10.** *Cont*.

buildings.

(**e**) (**f**)

**Figure 10.** GIS analysis of additional criteria: (**a**) electricity supply; (**b**) water supply and drainage; (**c**) road network; (**d**) construction density; (**e**) importance factor (public, school, etc.); (**f**) historical

**Figure 11.** Homogenous zones input data for PROMETHEE method (decision matrix).

All the above-mentioned data have been collected into the decision matrix to be used by the PROMETHEE method with the help of Visual PROMETHEE software (Figure 11).

*Appl. Sci.* **2022**, *12*, x FOR PEER REVIEW 12 of 23

**Figure 10.** GIS analysis of additional criteria: (**a**) electricity supply; (**b**) water supply and drainage; (**c**) road network; (**d**) construction density; (**e**) importance factor (public, school, etc.); (**f**) historical buildings. **Figure 10.** GIS analysis of additional criteria: (**a**) electricity supply; (**b**) water supply and drainage; (**c**) road network; (**d**) construction density; (**e**) importance factor (public, school, etc.); (**f**) historical buildings. **Figure 10.** GIS analysis of additional criteria: (**a**) electricity supply; (**b**) water supply and drainage; (**c**) road network; (**d**) construction density; (**e**) importance factor (public, school, etc.); (**f**) historical buildings.

All the above-mentioned data have been collected into the decision matrix to be used by the PROMETHEE method with the help of Visual PROMETHEE software (Figure 11). All the above-mentioned data have been collected into the decision matrix to be used by the PROMETHEE method with the help of Visual PROMETHEE software (Figure 11). All the above-mentioned data have been collected into the decision matrix to be used by the PROMETHEE method with the help of Visual PROMETHEE software (Figure 11).

**Figure 11.** Homogenous zones input data for PROMETHEE method (decision matrix). **Figure 11. Figure 11.** Homogenous zones input data for PR Homogenous zones input data for PROMETHEE method (decision matrix). OMETHEE method (decision matrix).

The preliminary results of the PROMETHEE method are given in Figure 12, in which better rank represents higher risk. It means that the best-ranked homogenous zone HZ13 has the highest seismic risk in this case.

*Appl. Sci.* **2022**, *12*, x FOR PEER REVIEW 13 of 23

has the highest seismic risk in this case.

has the highest seismic risk in this case.

The preliminary results of the PROMETHEE method are given in Figure 12, in which better rank represents higher risk. It means that the best-ranked homogenous zone HZ13

The preliminary results of the PROMETHEE method are given in Figure 12, in which better rank represents higher risk. It means that the best-ranked homogenous zone HZ13

Visual representation of the results has been made in GIS, where green represents low risk and red represents high risk (Figure 13). Visual representation of the results has been made in GIS, where green represents low risk and red represents high risk (Figure 13). higher risk). Visual representation of the results has been made in GIS, where green represents low risk and red represents high risk (Figure 13).

**Figure 13.** Seismic risk assessment for homogenous zones (green represents low risk, red represents high risk). **Figure 13.** Seismic risk assessment for homogenous zones (green represents low risk, red represents high risk). **Figure 13.** Seismic risk assessment for homogenous zones (green represents low risk, red represents high risk).

#### *3.2. Multi-Hazard Risk Asssessment of Homogenous Zones 3.2. Multi-Hazard Risk Asssessment of Homogenous Zones 3.2. Multi-Hazard Risk Asssessment of Homogenous Zones*

Three natural-hazards—seismic, flood and extreme waves—are combined and evaluated together to assess the multi-hazard risk, and the analysis is made on the level of homogenous zones. Three natural-hazards—seismic, flood and extreme waves—are combined and evaluated together to assess the multi-hazard risk, and the analysis is made on the level of homogenous zones. Three natural-hazards—seismic, flood and extreme waves—are combined and evaluated together to assess the multi-hazard risk, and the analysis is made on the level of homogenous zones.

The two combined risk analysis are made: combination of two risks for seismic and flood hazard; and combination of three risks for seismic, flood and extreme waves hazard. Each analysis is made on three levels. The first level of analysis is based on hazard and

vulnerability data aggregated for each homogenous zone. A second and third level of analysis are using additional criteria for each homogenous zone (Table 3).

**Table 3.** Combined risks analysis and criteria for each level of analysis.

Therefore, six multicriteria analyses are made on three different levels for each of the two scenarios. These multi-criteria analyses are classifying homogenous zones in accordance with multi-hazard risk.

The first analysis is the combined seismic-flood risk (Scenario S-F) on three different levels. Each level represents different criteria sets. Criteria are grouped in two major groups: main criteria, which are related to hazard and vulnerability and additional criteria, which are related to some important spatial data. Each criteria group has its own weight. In this case, an equal weight is given to each group—50%. An example of distribution of the criteria weights within the group are presented in Table 4. Criteria weights in this particular application are estimated comparing the estimated Expected Annual Damage values for each observed natural hazard. The Expected Annual Damage concept is based on the combination of occurrence probability and corresponding damage caused by each natural hazard [48], and it has proved to be an effective method since it enables a practical comparison of significantly different natural phenomena.

**Table 4.** An example of criteria weights for combined seismic-flood risk for Scenario S-F Level 2. **Table 4.** An example of criteria weights for combined seismic-flood risk for Scenario S-F Level 2.

enables a practical comparison of significantly different natural phenomena.

caused by each natural hazard [48], and it has proved to be an effective method since it

*Appl. Sci.* **2022**, *12*, x FOR PEER REVIEW 15 of 23

The input data for analysis is presented as a matrix with alternatives, in this case 14 homogenous zones (HZ) and up to 11 criteria depending on the level of analysis (Figure 14). The input data for analysis is presented as a matrix with alternatives, in this case 14 homogenous zones (HZ) and up to 11 criteria depending on the level of analysis (Figure 14).

**Figure 14.** Input matrix for combined seismic-flood risk (Scenario S-F) multi-criteria analysis with criteria evaluation for all three levels. **Figure 14.** Input matrix for combined seismic-flood risk (Scenario S-F) multi-criteria analysis with criteria evaluation for all three levels.

The input data (Figure 14) and criteria weights (Table 4) are imported in a multicriteria analysis application based on the PROMETHEE method and results have been calculated for all three levels of analysis. The first analysis was made for the criteria set defined as Level 1, a second analysis for Level 2 and a third for Level 3. The criteria for each analysis were submitted to PROMETHEE method and the results for all three levels are presented in Figures 15, 16 and 17, respectively. There are no significant variations in results except zone HZ 13, which becomes more exposed when additional criteria are used (Level 1 and 2). At the end, the results are exported into GIS for better visualization and a further analysis of results (Figure 18). The input data (Figure 14) and criteria weights (Table 4) are imported in a multi-criteria analysis application based on the PROMETHEE method and results have been calculated for all three levels of analysis. The first analysis was made for the criteria set defined as Level 1, a second analysis for Level 2 and a third for Level 3. The criteria for each analysis were submitted to PROMETHEE method and the results for all three levels are presented in Figures 15–17, respectively. There are no significant variations in results except zone HZ 13, which becomes more exposed when additional criteria are used (Level 1 and 2). At the end, the results are exported into GIS for better visualization and a further analysis of results (Figure 18).

The second analysis is a combined seismic–flood–extreme waves risk (Scenario S-F-EW) on three different levels. Again, each level represents different criteria sets, and criteria are grouped into two groups: main criteria, which are related to hazard and additional criteria, which are related to some important spatial data. Each criteria group has its own weight. In this case, an equal weight is given to each group: 50%. Other criteria weights are presented in Table 5. The input data for analysis are presented as a matrix with alternatives, in this case 14 homogenous zones (HZ) and up to 13 criteria depending on the level of analysis (Figure 19).

*Appl. Sci.* **2022**, *12*, x FOR PEER REVIEW 16 of 23

*Appl. Sci.* **2022**, *12*, x FOR PEER REVIEW 16 of 23

**Figure 15.** Results of risk analysis for seismic and flood hazard for Level 1 (Scenario S-F L1). **Figure 15.** Results of risk analysis for seismic and flood hazard for Level 1 (Scenario S-F L1). **Figure 15.** Results of risk analysis for seismic and flood hazard for Level 1 (Scenario S-F L1).

**Figure 15.** Results of risk analysis for seismic and flood hazard for Level 1 (Scenario S-F L1).

**Figure 17.** Results of risk analysis for seismic and flood hazard for Level 3 (Scenario S-F L3). **Figure 17.** Results of risk analysis for seismic and flood hazard for Level 3 (Scenario S-F L3).

**Figure 18.** GIS visualization of risk for seismic and flood hazard for Level 3 (Scenario S-F L3). **Figure 18.** GIS visualization of risk for seismic and flood hazard for Level 3 (Scenario S-F L3).

Seismic vulnerability (1.2) 19.5%

The second analysis is a combined seismic–flood–extreme waves risk (Scenario S-F-EW) on three different levels. Again, each level represents different criteria sets, and **Table 5.** An example of criteria weights for combined seismic–flood–extreme waves risk for Scenario S-F-EW Level 2.

50% Flood hazard (1.3) 2.5% Flood vulnerability (1.4) 2.5% Extreme waves hazard (1.5) 3.0% Extreme waves vulnerability (1.6) 3.0% Additional criteria *(n—number of additional criteria)* 50% Construction density (2.1) 50/*n* = 12.5% Inhabitation density (2.2) 50/*n* = 12.5% Importance factor (2.3) 50/*n* = 12.5% Historical buildings (2.4) 50/*n* = 12.5% The input data (Figure 19) and criteria weights (Table 5) are imported in a multi-criteria analysis application based on PROMETHEE method and results have been calculated for all three levels of analysis. The first analysis was made for a criteria set defined as Level 1, a second analysis for Level 2 and a third for Level 3. The criteria for each analysis were submitted to PROMETHEE method and the results for all 3 levels are presented in Figures 20–22, respectively. Again, there are no significant variations in results except zone HZ 13, which becomes more exposed when additional criteria are used (Level 1 and 2). At the end, the results are exported into GIS for better visualization and a further analysis of results (Figure 23).

*Appl. Sci.* **2022**, *12*, x FOR PEER REVIEW 18 of 23

presented in Figures 20, 21 and 22, respectively. Again, there are no significant variations

**Figure 19.** Input matrix for combined seismic–flood–extreme waves risk (Scenario S-F-EW) multicriteria analysis with criteria evaluation for all three levels. **Figure 19.** Input matrix for combined seismic–flood–extreme waves risk (Scenario S-F-EW) multicriteria analysis with criteria evaluation for all three levels. in results except zone HZ 13, which becomes more exposed when additional criteria are used (Level 1 and 2). At the end, the results are exported into GIS for better visualization and a further analysis of results (Figure 23).

**Figure 20.** Results of the risk analysis for seismic, flood and extreme waves hazards for Level 1 (Scenario S-F-EW L1). **Figure 20.** Results of the risk analysis for seismic, flood and extreme waves hazards for Level 1 (Scenario S-F-EW L1). *Appl. Sci.* **2022**, *12*, x FOR PEER REVIEW 19 of 23

**Figure 21.** Results of the risk analysis for seismic, flood and extreme waves hazards for Level 2 (Scenario S-F-EW L2). **Figure 21.** Results of the risk analysis for seismic, flood and extreme waves hazards for Level 2 (Scenario S-F-EW L2).

**Figure 22.** Results of the combined risk analysis for seismic, flood and extreme waves hazards for

Level 3 (Scenario S-F-EW L3).

(Scenario S-F-EW L2).

**Figure 22.** Results of the combined risk analysis for seismic, flood and extreme waves hazards for Level 3 (Scenario S-F-EW L3). **Figure 22.** Results of the combined risk analysis for seismic, flood and extreme waves hazards for Level 3 (Scenario S-F-EW L3). *Appl. Sci.* **2022**, *12*, x FOR PEER REVIEW 20 of 23

**Figure 21.** Results of the risk analysis for seismic, flood and extreme waves hazards for Level 2

**Figure 23.** GIS visualization of risk for seismic, flood and extreme waves hazard for Level 3 (Scenario **Figure 23.** GIS visualization of risk for seismic, flood and extreme waves hazard for Level 3 (Scenario S-F-EW L3).

#### **4. Conclusions 4. Conclusions**

S-F-EW L3).

threats.

The multi-hazard risk assessment of urban areas is an important step in the risk management process. It can be used to reduce, manage and overcome the risks arising from the combination of different multiple hazards. This paper uses Spatial Multi-Criteria Decision-Making based on PROMETHEE method, coupled with the Geographic Information System, to assess the single-hazard and multi-hazard risk caused by seismic, sea floods and extreme sea waves. A case study for the application of the method is Kaštel Kambelovac, an urban settlement placed at the Croatian part of the Adriatic coast. The observed area has been divided into the homogenous zones that have been identified as areas of the test site with some mutual spatial characteristics. The homogeneity was The multi-hazard risk assessment of urban areas is an important step in the risk management process. It can be used to reduce, manage and overcome the risks arising from the combination of different multiple hazards. This paper uses Spatial Multi-Criteria Decision-Making based on PROMETHEE method, coupled with the Geographic Information System, to assess the single-hazard and multi-hazard risk caused by seismic, sea floods and extreme sea waves. A case study for the application of the method is Kaštel Kambelovac, an urban settlement placed at the Croatian part of the Adriatic coast. The observed area has been divided into the homogenous zones that have been identified as areas of the test site with some mutual spatial characteristics. The homogeneity was identified by intersecting spatial

specificity of this research is multi-hazard risk evaluation based on a previous detailed calculation of vulnerability indexes of each building in the test area for all observed

The methodology ranks the various homogenous zones in terms of the relative proneness to coupled seismic, sea floods and extreme sea waves hazards. It enables a risk analysis for different scenarios for both single and multiple hazards at the level of buildings and homogenous zones. In the case study presented here, the analysis has shown that the seismic risk is a dominant threat in all scenarios. The results of a multihazard analysis for the combination of seismic and sea floods hazard have shown that the area with the highest risk is related to the historical part of Kaštel Kambelovac. This is due to the fact that this area, along with high seismic risk, has the highest level of exposure to flooding. Furthermore, the vulnerability of exposed objects in the historical part is highest for both hazards. Likewise, the results of the multi-hazard analysis for the combination of seismic, sea floods and extreme waves hazard are analogous to previous ones, showing that the highest risk is again in the historical part of Kaštel Kambelovac. The vulnerability

identified by intersecting spatial layers in the GIS. The multi-hazard risk assessment

layers in the GIS. The multi-hazard risk assessment method is based on the validation of the main components of the risk caused by natural hazard phenomena, such us vulnerability expressed in terms of the vulnerability index, hazard and influence of additional criteria, to overall risk in homogenous zones. The main specificity of this research is multi-hazard risk evaluation based on a previous detailed calculation of vulnerability indexes of each building in the test area for all observed threats.

The methodology ranks the various homogenous zones in terms of the relative proneness to coupled seismic, sea floods and extreme sea waves hazards. It enables a risk analysis for different scenarios for both single and multiple hazards at the level of buildings and homogenous zones. In the case study presented here, the analysis has shown that the seismic risk is a dominant threat in all scenarios. The results of a multi-hazard analysis for the combination of seismic and sea floods hazard have shown that the area with the highest risk is related to the historical part of Kaštel Kambelovac. This is due to the fact that this area, along with high seismic risk, has the highest level of exposure to flooding. Furthermore, the vulnerability of exposed objects in the historical part is highest for both hazards. Likewise, the results of the multi-hazard analysis for the combination of seismic, sea floods and extreme waves hazard are analogous to previous ones, showing that the highest risk is again in the historical part of Kaštel Kambelovac. The vulnerability of exposed objects to extreme waves is the highest in the historical part, and this particular area is most exposed to extreme waves due its low-lying coast. Performed analyses provide useful information for decision makers and public authorities to define priorities in future interventions through the process of risk management planning.

**Author Contributions:** Conceptualization, Ž.N. and N.M.; data curation, M.M., N.M. and T.K.; funding acquisition, Ž.N. and E.B.; investigation, Ž.N., N.M., M.M., E.B. and T.K.; methodology, N.M. and M.M. and Ž.N.; software, N.M. and M.M.; supervision, Ž.N. and E.B.; validation, Ž.N., N.M., M.M., E.B. and T.K.; visualization, M.M. and T.K.; writing—original draft, Ž.N., N.M., M.M., E.B. and T.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the European Union, Programme Interreg Italy–Croatia, Project "Preventing, managing and overcoming natural-hazards risks to mitigate economic and social impact"—PMO-GATE ID 10046122. The research is also partially supported through project KK.<IP_ADDRESS>.0027, co-financed by the Croatian Government and the European Union through the European Regional Development Fund—the Competitiveness and Cohesion Operational Programme.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Some of the datasets that were analyzed in this research are publicly available on the website of PMO-GATE project: https://www.italy-croatia.eu/web/pmo-gate (accessed on 25 October 2022).

**Acknowledgments:** This research was part of the Work Packages 4 and 5 of the European Union, Programme Interreg Italy–Croatia, Project "Preventing, managing and overcoming natural-hazards risks to mitigate economic and social impact"—PMO-GATE ID 10046122. The results of this research were published in the Deliverables: "4.1.1. Methodology for provision assessment indexes based on Spatial Multi-Criteria Decision-Making—Croatia" and "5.1.6. Map of spatial distribution of the critical zones most prone to flood, extreme waves and seismic risks for HR test site".

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


**Farmers' Net Income Distribution and Regional Vulnerability to Climate Change: An Empirical Study of Bangladesh**

#### **Md. Shah Alamgir 1,2, Jun Furuya 3,\*, Shintaro Kobayashi 3, Mostafiz Rubaiya Binte 1 and Md. Abdus Salam 4**

Received: 14 June 2018; Accepted: 16 July 2018; Published: 23 July 2018

**Abstract:** Widespread poverty is the most serious threat and social problem that Bangladesh faces. Regional vulnerability to climate change threatens to escalate the magnitude of poverty. It is essential that poverty projections be estimated while bearing in mind the effects of climate change. The main purpose of this paper is to perform an agrarian sub-national regional analysis of climate change vulnerability in Bangladesh under various climate change scenarios and evaluate its potential impact on poverty. This study is relevant to socio-economic research on climate change vulnerability and agriculture risk management and has the potential to contribute new insights to the complex interactions between household income and climate change risks to agricultural communities in Bangladesh and South Asia. This study uses analysis of variance, cluster analysis, decomposition of variance and log-normal distribution to estimate the parameters of income variability that can be used to ascertain vulnerability levels and help us to understand the poverty levels that climate change could potentially generate. It is found that the levels and sources of income vary greatly among regions of Bangladesh. The variance decomposition of income showed that agricultural income in Mymensingh and Rangpur is the main cause of the total income difference among all sources of income. Moreover, a large variance in agricultural income among regions is induced by the gross income from rice production. Additionally, even in the long run the gradual, constant reduction of rice yield due to climate change in Bangladesh is not a severe problem for farmers. However, extreme events such as floods, flash floods, droughts, sea level rise and greenhouse gas emissions, based on Representative concentration pathways (RCPs), could increase the poverty rates in Mymensingh, Rajshahi, Barisal and Khulna—regions that would be greatly affected by unexpected yield losses due to extreme climatic events. Therefore, research into and development of adaptation measures to climate change in regions where farmers are largely dependent on agricultural income are important.

**Keywords:** income distribution; cost distribution; vulnerable region; adaptation measures; Bangladesh


**4. Previously extracted bioactive compounds from plants using MAE technique**

MAE has been employed in several ways to extract bioactive compounds from different plant samples; the isolates from these plant samples are being used in nutraceutical and pharmaceutical applications. Microwave irradiation is mostly used to resolve some of the drawbacks associated with traditional methods. **Table 2** presents some of the previous studies that employed MAE to extractive bioactive compounds from plant samples. In the presented results obtained from previous studies as presented in **Table 2**, it can be seen the use of microwave-assisted extraction technique recover improved quantities of global yields, different phenolic compounds, and bioactive compounds. These indicated the efficacy of MAE over other methods of extractions.

### **5. Conclusions**

This chapter outlines the studies and many advances in development in the MAE of a number of plant compounds. The factors that influence the performance of MAE technique have been extensively discussed as well as some of the bioactive compounds previously reported from plant samples using the MAE. The previously reported results showed that MAE can recover higher yields of bioactive compounds relative to other extraction methods. Thus, MAE is a promising method in achieving substantial bioactive compounds from plant materials due to its importance over other techniques.

**Author details**

Abdurahman Hamid Nour1

Pahang-UMP, Malaysia

Pahang-UMP, Malaysia

<EMAIL_ADDRESS>
**27**

provided the original work is properly cited.

Khartoum, Sudan

Manal Suliman Omer3 and Noormazlinah Ahmed4

*Microwave-Assisted Extraction of Bioactive Compounds (Review)*

*DOI: http://dx.doi.org/10.5772/intechopen.96092*

\*Address all correspondence to<EMAIL_ADDRESS>
\*, Alara Ruth Oluwaseun1

1 Department of Chemical Engineering, College of Engineering, Universiti Malaysia

2 Faculty of Pure and Applied Sciences, International University of Africa,

3 Faculty of Industrial Management, Universiti Malaysia Pahang-UMP, Malaysia

4 Faculty of Chemical and Process Engineering Technology, Universiti Malaysia

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

, Azhari Hamid Nour2

,


*4.1. Dietary Patterns*

Overall, there is evidence that certain dietary patterns may influence the development and progression of anxiety disorders. The diets associated with lower anxiety include "healthy" diet patterns, the Mediterranean diet, traditional diets, the anti-inflammatory diet, and diets with increased variety. All of these diet patterns share common elements such as an emphasis on vegetables, fruit, limited sugar and refined grains, and greater consumption of minimally processed foods. However, interpretation of dietary patterns studies is somewhat hindered by the dissimilar definitions used for dietary patterns. For example, in the studies delivering "Western" style high fat/high sugar diets meant to induce obesity, a variety of dietary fats were used. The fats used to supplement some of the high fat diets included lard [67], fish oil [68], soybean oil [69] while other studies specified the percentage fat in the diet but not the type of fat that was added to achieve this amount [70–72]. Because the impact of different fatty acids on health outcomes can be highly different [73], categorizing diets as low or high in fat may result in heterogenous findings as a result of the type of fat delivered. Similarly, definitions of "healthy" diets have changed over time [74], potentially contributing to the heterogeneity of study results. Unfortunately, many studies lacked clear definitions of "healthy" or "unhealthy" diet patterns and interventions.

The outcomes associated with the vegetarian or vegan diet were generally positive although somewhat mixed and limited by being largely observational in design. The mixed findings may be due to a variety of factors. There is documentation of the adoption of a vegetarian or vegan diet following the development of an eating disorder [75]. Given the association between eating disorders and anxiety disorders [76], this may explain the association between vegetarianism and higher anxiety found in some observational studies. Furthermore, vegan diets, without adequate supplementation, may lack certain essential nutrients shown to play a role in anxiety disorders such as vitamin B12 [77] and long chain omega-3 fatty acids (EPA and DHA) [78]. Bioavailability of certain nutrients such as iron differ between plant and animal sources, possibly limiting nutrient absorption in vegan diets.

There is a significant lack of human intervention studies involving participants with anxiety disorders or elevated baseline anxiety symptoms. Many studies employed dietary patterns that were indicated for the other medical concerns of the study participants. For example, studies delivered the Low FODMAP diet to participants with irritable bowel syndrome and hypocaloric diets to participants with obesity and assessed changes in anxiety as secondary outcomes. The mechanisms by which dietary patterns impact anxiety symptoms may be the result of a combination of the mechanistic factors discussed in the following sections.


*2.1. Study Area*

The study area lies between 68◦04 00~104◦40 00 E and 27◦25 30~40◦08 00 N (Figure 2), which extends from the southern margin of the Himalaya to the northern margin of the Kunlun and Qilian Mountains, and the Pamirs Plateau and Karakorum Mountains in the west to the Hengduan Mountains in the east [55]. This area has the highest elevation (more than 4000 m above sea level) and diverse topography, which support a variety of vegetation types at different elevations. From low to high elevations, these vegetation types include dry and hot river valley scrubs, broadleaved forests, mixed coniferous broadleaved forests, coniferous forests, shrubs, meadows, cold subnival belts, and nival belts (glaciers). This region harbors the world's most species-rich temperate alpine flora driven by orogenetic movements and climate change (monsoon intensification) [56]. It is currently home to approximately 12,000 species of vascular plants belonging to 1500 genera, of which more than 20% are endemic [57]. The mountain region is a diversity center for many species-rich genera, such as rhododendron, gentiana, saussurea, and pedicularis, all of which contain numerous endemic species [14]. We divided the entire study area into five subregions based on the boundaries of the Qinghai-Tibet Plateau, Hengduan Mountains, and Hindu-Kush Himalaya: plateau platform, Hengduan Mountains, eastern Himalaya, central Himalaya, and western Himalaya (Figure 2) [10,11,55]. Of these, the alpine plant biota in Hengduan Mountains is the richest [56].

**Figure 2.** The presence points of *Meconopsis* species in the study area.


**5. Evaluation and optimization of the topology via ambiguity function analysis**

As discussed in the chapter, sensor network imaging is one of the important applications of UWB sensors. In the UWB sensor network, there are stationary sensors (e.g. anchors), and mobile sensors. In the perspective of radar imaging, the spatial distribution of the stationary sensors would form a "real array", while the movement of the mobile sensors would generate a "virtual array" (i.e. synthetic aperture). The beam patterns of this "real array" and "virtual array" highly depend on their spatial configurations (topologies). In other words, the resolving performance of the real/virtual array highly depends on the topology itself.

Beyond the topologies of the real and virtual arrays, the signal parameters such as the waveform, bandwidth, etc., could also impact the resolving performance of the system. That is, the overall resolving performance of the system is jointly decided by the parameters, including the topologies of the real and virtual arrays, as well as the signal parameters. This makes the analysis of the topology even more challenging.

Sensor networks are designed to be highly accurate for their intended purpose. Always, designers and engineers are required to know the level of resolution expected from a particular sensor configuration. In order to evaluate and optimize the topologies, ambiguity function analysis is introduced in this section. Via ambiguity functions, we could know how the topology of the real/virtual array (i.e. the array formed by stationary/mobile sensors) contribute to the resolving performance of the system, and then further optimize it [30, 69].

Generally, in the far field, the ambiguity function can be factorized into several factors such as the signal related factor, the topology factor (associated with the "real array"), and the motion factor (associated with the "virtual array") [69], as shown in Fig. 16. The combination of these factors results in the overall resolving ability of the system. Theoretically, each individual factor can be used to evaluate a certain aspect of the resolution characteristics or optimize certain parameters instead of using the complicated ambiguity function of the system as a whole.

As described in the scenario, a number of UWB sensors are deployed to image the environment in order to provide necessary information for further applications. As shown in Fig. 17, it can consist of a number of moving transmitters (*Ti* ∈ {*T*1, *T*2, .., *TM*} ) and a number of stationary receivers (*Rj* ∈ {*R*1, *R*2, .., *RN*} ) . In this way, a UWB sensor network is constructed. The transmitters can move along predefined tracks (e.g. Track 1 & 2) to probe

20 Will-be-set-by-IN-TECH

 between **transmitter array**  & **receiver array**

Evaluation To evaluate the contribution of each individual factor

Topology factor Receiver placement

Motion factor the receiver array

Optimization To optimize a certain group of parameters via the corresponding factor

Transmitter placement **within** the transmitter array

**within**

Signal factor Relative position

**Figure 16.** Factorization of MIMO ambiguity function and its potential applications.

makes the analysis of the topology even more challenging.

**5. Evaluation and optimization of the topology via ambiguity function**

As discussed in the chapter, sensor network imaging is one of the important applications of UWB sensors. In the UWB sensor network, there are stationary sensors (e.g. anchors), and mobile sensors. In the perspective of radar imaging, the spatial distribution of the stationary sensors would form a "real array", while the movement of the mobile sensors would generate a "virtual array" (i.e. synthetic aperture). The beam patterns of this "real array" and "virtual array" highly depend on their spatial configurations (topologies). In other words, the resolving performance of the real/virtual array highly depends on the topology itself.

Beyond the topologies of the real and virtual arrays, the signal parameters such as the waveform, bandwidth, etc., could also impact the resolving performance of the system. That is, the overall resolving performance of the system is jointly decided by the parameters, including the topologies of the real and virtual arrays, as well as the signal parameters. This

Sensor networks are designed to be highly accurate for their intended purpose. Always, designers and engineers are required to know the level of resolution expected from a particular sensor configuration. In order to evaluate and optimize the topologies, ambiguity function analysis is introduced in this section. Via ambiguity functions, we could know how the topology of the real/virtual array (i.e. the array formed by stationary/mobile sensors) contribute to the resolving performance of the system, and then further optimize it [30, 69]. Generally, in the far field, the ambiguity function can be factorized into several factors such as the signal related factor, the topology factor (associated with the "real array"), and the motion factor (associated with the "virtual array") [69], as shown in Fig. 16. The combination of these factors results in the overall resolving ability of the system. Theoretically, each individual factor can be used to evaluate a certain aspect of the resolution characteristics or optimize certain parameters instead of using the complicated ambiguity function of the system as a

As described in the scenario, a number of UWB sensors are deployed to image the environment in order to provide necessary information for further applications. As shown in Fig. 17, it can consist of a number of moving transmitters (*Ti* ∈ {*T*1, *T*2, .., *TM*} ) and a number of stationary receivers (*Rj* ∈ {*R*1, *R*2, .., *RN*} ) . In this way, a UWB sensor network is constructed. The transmitters can move along predefined tracks (e.g. Track 1 & 2) to probe

**MIMO Ambiguity Function Factorization**

**analysis**

whole.

Factors

Applicati ons

**Figure 17.** UWB MIMO imaging scenario. "Track 1 & 2" are the transmitter tracks; Triangles: nonlinear tracks.

the environment. The receivers collect the backscattered probing signals to produce an image of the environment. Meanwhile, they could also serve as anchor sensors to support other applications, such as localizing and tracking the position of the moving transmitters [61].

The sensor motion factor is shown in Fig. 18 (a) and (b), with respect to different tracks (Track1 and Track2, as defined in Fig. 17). In the figures, apparently, the ripples are narrower in the direction of L1 compared to the direction of L2. It indicates that the resolving performance in the direction of L1 is better than that of L2, due to the total angular rotation in the direction of L1 is far greater than the angular rotation in the direction of L2 with respect to the reference **x**0. For similar reasons, the resolving performance of "Track1" is better than the resolving performance of "Track2" in the corresponding directions. In addition, it is shown in Fig. 18 (a) that a "ghost" object occurs in the direction of L2, due to an insufficient illumination of the object. Generally, it would generate a false object image, and consequently worsen the quality of the image.

In Fig. 18 (a) and (b), the motion factors are given with respect to linear tracks. However, in practice, the sensors are not necessarily moving along linear tracks. There may be more practical irregular tracks as shown in Fig. 17 where the triangles indicate the transmission positions. The irregular movement of the sensors could improve the performance of ghost suppression, since the irregular tracks can provide a more sufficient illumination of the environment compared to the linear tracks.

According to the sensor topology in Fig. 17, the topology factor is given in Fig. 18(c). In the figure, the ghost image is partially suppressed. As shown in the figure, the suppression residuals exist at the ghost image position. However, they are not as strong as the real object. Theoretically, the ghost image can be further suppressed by optimizing the sensor spatial placement.

Figure 18 (a), (b) and (c) indicate the resolution contribution of the sensor motions and the sensor placement topology to the overall resolution. As given in Fig. 17, the overall performance of the system is the combination of all involved individual factors. It implies that we can try to realize a better overall resolving performance by (i) optimizing each individual factor, or (ii) trading-off between related factors. For example, in order to suppress the "ghost" image, on the one hand, we can optimize the movement tracks via the motion factor and the sensor placement topology via the topology factor. On the other hand, a compromise can be made between the motion factor and the topology factor. In this sense, due to the interaction

22 Will-be-set-by-IN-TECH 200 Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications Cooperative Localization and Object Recognition in Autonomous UWB Sensor Networks 23

**6.1. Measurement model**

(a) single reflection (b) double reflection (c) scatterer

Cooperative Localization and Object Recognition in Autonomous UWB Sensor Networks 201

To detect and localize the features, the bat-type UWB radar is used to measure the round-trip-times between the transmitting antenna, features of the surroundings, and the receiving antennas, which are extracted from UWB impulse responses and stored in the measurement vector **z**. A two-dimensional geometrical model of the real world is used in the algorithm, similar to [4]. Walls and corners are represented as single and double reflections, respectively. Edges and small objects are represented as scatterers. Schematic illustrations of

Using an estimated initial position and orientation of the antenna array and an estimated initial position of a feature, an expected time-of-flight between transmitter, feature and receiver can be calculated. This can be used for dynamic state estimation, for example in an Extended Kalman filter or a particle filter, to iteratively improve the estimate of the positions. By measuring at different positions or rotating the antenna array, it is possible to distinguish the different features and calculate an initial estimate of their positions. To do this,

To solve the SLAM problem, a state-space description is used. The state vector **x** to be

**x***robot* contains the information about the robot position in *x* and *y* direction, *px* and *py*, as well as the speed of the robot, represented as movement angle *pφ* and the absolute value of the

*px*, *py*, *pφ*, *v*

**x***robot*, **x***sensor*, **x***mapT* (7)

*T* (8)

**Figure 19.** Three room features used in the state-space measurement equation.

the propagation models are shown in Fig. 19 .

estimated consists of three different parts.

a state-space description of the room and the robot is needed.

**6.2. State-space description of the room and the robot**

speed *v*. All values are in relation to the local coordinate system.

**x** =

**xrobot** =

**Figure 18.** The motion and topology factors given at a certain frequency *f* . For the motion factor, *v* · *PRT* = 5*c*/ *f* , where *v* is the sensor speed, *PRF* is the pulse repetition time, and *c* is the signal propagation speed. For the topology factor, the sensor element interval is 5*c*/ *f* , and the number of sensor elements on each array is 30.

of the factors, the sensor network provides more degrees of freedom for the system designer, compared with a single sensor system.


**9. Summary points**

In vitro maturation of oocytes is a simple and less stressful process of treatment. Main indication: PCOS patients, selected cases with fertilization failure, and fertility preservation.

**187**

**Author details**

and Yona Barak6,7

Israel

Adrian Ellenbogen1,2,3\*, Einat Shalom Paz2

6 Dr Yona Barak Laboratories Ltd., Israel

provided the original work is properly cited.

2 IVF Unit, Hillel Yaffe Medical Center, Hadera, Israel

3 Meuhedet Female Health Center, Bnai Brak, Israel

7 Alpha Scientists in Reproductive Medicine, Israel

\*Address all correspondence to<EMAIL_ADDRESS>
4 IVF Laboratory, Hillel Yaffe Medical Center, Hadera, Israel

5 IVF & Reproductive Genetics Centre, Moscow, Russian Federation

*In Vitro Maturation and Fertilization of Oocytes: From Laboratory Bench to Clinical Practice*

, Medeia Michaeli4

1 Rapport School of Medicine, Technion—Israel Institute of Technology, Haifa,

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

, Anna Smirnova5

*DOI: http://dx.doi.org/10.5772/intechopen.91802*

Pregnancy rates are comparable.

*In Vitro Maturation and Fertilization of Oocytes: From Laboratory Bench to Clinical Practice DOI: http://dx.doi.org/10.5772/intechopen.91802*


18. Conclusion

PUD is a common clinical problem. The two most important risk factors are H. pylori infection and NSAIDs. Patients may present with dyspepsia or may remain asymptomatic. Endoscopy is the gold standard for the diagnosis of PUD. But as it is not possible to endoscope so many dyspeptic patients, there are some non-endoscopic approaches depending on the prevalence of H. pylori infection in the population. But ACG and CAG recommend EGD to be done in patients ≥60 years of age presenting with dyspepsia irrespective of alarm features. Bismuth quadruple therapy or concomitant therapy should be considered as the first-line therapy against H. pylori infection. In patients with PUD, eradication of H. pylori infection (if positive) should be confirmed ≥4 weeks after completion of therapy. PPI, H2RA, misoprostol, and sucralfate are the main agents used for healing of PU. Surveillance endoscopy is recommended in certain gastric ulcers. PUD can be complicated by bleeding, perforation, penetration, and gastric outlet obstruction. Patients with bleeding peptic ulcer should be evaluated, resuscitated, and started on intravenous/infusion of PPI. Diagnostic and therapeutic endoscopy should be done to achieve endoscopic hemostasis. If endoscopic therapy fails, the next step will be TAE or surgery. The mortality for peptic ulcer bleeding still remains high.

Author details

Peptic Ulcer Disease

DOI: http://dx.doi.org/10.5772/intechopen.86652

Monjur Ahmed

17

Jefferson University Hospital, Philadelphia, USA

provided the original work is properly cited.

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Peptic Ulcer Disease DOI: http://dx.doi.org/10.5772/intechopen.86652

16. Penetration

Digestive System - Recent Advances

surgical intervention.

18. Conclusion

remains high.

16

17. Gastric outlet obstruction (GOO)

treatment need endoscopic dilation or surgery [82, 83].

When the ulcer crater erodes through the gastric wall or intestinal wall into the surrounding structure but there is no free perforation or leakage of luminal contents

It occurs in less than 5% cases of PUD. Duodenal ulcer and pyloric channel ulcer

are generally associated with GOO. Pathophysiologically, reversible causes like inflammation, edema, spasm, and pyloric dysmotility and irreversible cause like fibrosis may lead to GOO. Patients present with nausea, vomiting, early satiety, epigastric pain, and weight loss. Patients may develop severe dehydration, azotemia, hyponatremia, and hypochloremic and hypokalemic metabolic alkalosis with paradoxical aciduria due to prolonged vomiting. First, the fluid and electrolyte deficit should be corrected. Gastric contents should be removed by large-bore Ewald tube, and then intermittent nasogastric tube suction should be continued for a few days. Many cases of GOO due to PUD have reversible components which may respond to this conservative treatment. Patients not responding to the conservative

PUD is a common clinical problem. The two most important risk factors are H. pylori infection and NSAIDs. Patients may present with dyspepsia or may remain asymptomatic. Endoscopy is the gold standard for the diagnosis of PUD. But as it is not possible to endoscope so many dyspeptic patients, there are some non-endoscopic approaches depending on the prevalence of H. pylori infection in the population. But ACG and CAG recommend EGD to be done in patients ≥60 years of age presenting with dyspepsia irrespective of alarm features. Bismuth quadruple therapy or concomitant therapy should be considered as the first-line therapy against H. pylori infection. In patients with PUD, eradication of H. pylori infection (if positive) should be confirmed ≥4 weeks after completion of therapy. PPI, H2RA, misoprostol, and sucralfate are the main agents used for healing of PU. Surveillance endoscopy is recommended in certain gastric ulcers. PUD can be complicated by bleeding, perforation, penetration, and gastric outlet obstruction. Patients with bleeding peptic ulcer should be evaluated, resuscitated, and started on intravenous/infusion of PPI. Diagnostic and therapeutic endoscopy should be done to achieve endoscopic hemostasis. If endoscopic therapy fails, the next step will be TAE or surgery. The mortality for peptic ulcer bleeding still

into the peritoneal cavity, it is called penetration [80]. The pancreas is the commonest site of penetration. Other sites of penetration include the omentum, biliary tract, liver, colon, mesocolon, and blood vessels. Patients may notice change in pattern of abdominal pain, i.e., pain not being relieved by taking food or medication. Diagnosis is confirmed by CT with contrast which may show loss of fascial plane between the gastric wall or intestinal wall and the surrounding structure, band of soft tissue density between them, ulcer crater, sinus tract, and enlargement of head of the pancreas in case of penetration into the pancreas [81]. Treatment is


**4.1 Pilot**

*Strategy and Behaviors in the Digital Economy*

points in mind.

**3.2 Hypothesis**

viewpoints.

sound.

experiments.

**4. Methods**

by image schema (e) in order to confirm existing meaning or concept of those words or sentences. When those words or sentences are unknown for subjects, they might refer to existing concepts which were constructed by perception of other organs, and then their own new concepts would be reconstructed (f) before decision-making for meaning of words or sentences (c) and adjusted by feedforward control (g).When you are citing sources, the citations should be set in a numbered format. All the references given in the list of references should be cited in the body of the text. Please set citations in square brackets keeping the below

There would be two types of traits hypothetically, visual type and auditory type, from the model of information processing. In the case of visual type, information might be mainly processing a circle of (a)→(b)→(c)→(d) repeatedly, and then finally decision-making would be done on (h)→(i) processing. On the other hand, in the case of auditory type, they might add another processing circle of (e)→(f)→(g). In this case, they might be referred to the existed concept which has been constructed by auditory information processing. From those

Hypothesis 1: In the case of auditory type, letters are supposed to be encoded to phonological sound. Consequently, the correlation coefficient between decision time and the number of words or duration of reading aloud (sound voice

Hypothesis 2: The decision time of visual type is faster than those of auditory type because the former ones are supposed to not transform words from letters to

In the next section, we will prove whether those hypotheses are correct or not by

Questionnaires of personality inventory for a psychological testing (YGPI) have been presented one by one as experimental subjects [Appendices 1 and 2]. YGPI consisted of 120 questionnaires with one short sentence each. Because of making the reliability of the test higher, those questionnaires are presented by reading aloud to subjects in order to fixed interval for selecting answers. It is important for a coefficient of confidence that subjects are brought about replying in time by

For that reason, in the case of testing by paper and pencil, questionnaires of YGPI are not presented by written letters, but sound voice. On the other hand, in the case of testing on display, even presenting them by written letters are controlled the interval of the same condition as sound voice. Accordingly, when we have developed measuring system for cognitive traits of language information processing in terms of written letters, we decided to regulate time of presenting, along

Participants are required to evaluate their behavior in everyday life whether the questionnaire is the same or not comparing with those of themselves and chose the answer among "yes," "no," and "either one" within 3 s after finishing the presentation of a questionnaire. As an instruction of testing, participants are also required to reply quickly without deliberating on making decision in order to prevent from no

presentation) might be higher than those of visual type.

effort because those regulations make their mental state similar.

with each questionnaire by sound voice.

**128**

choice within the time.

## *4.1.1 Purpose of pilot experiments*

The aim of pilot experiments is to find out what have been changed in digital society from cognitive aspects in education. Along with this purpose, experiments and analysis are planned. In the field of education, we are able to choose media, such as sound voice or written letters, using digital materials, for instance, digital textbook and electronic blackboard. Accordingly, we have planned experiments for two kinds of comparison between sheet (paper)/PC and sound voice/letters.

### *4.1.2 Experiment 1*

(1) Subjects: Three kinds of experiments (presenting questionnaires by sound voice and reply on the sheet (OCR), presenting questionnaires by sound voice and reply on display, and presenting questionnaires by written letters and reply on display). (2) Participants: 29 high school students of the first grade (male 13, female 16).

## *4.1.3 Experiment 2*

(1) Subjects: Three kinds of experiments (presenting questionnaires by written letters and reply on the sheet (OCR), presenting questionnaires by written letters and reply on display, and presenting questionnaires by sound voice and reply on display). (2) Participants: 7 university students (male 5, female 2).

#### **4.2 Preliminary experiments**

#### *4.2.1 Purpose of preliminary experiments*

The purpose of preliminary experiments is to validate reproducibility concerning with the calibration of measuring system, the method of testing, and the results of analysis (comparison between visual type and auditory type).

#### *4.2.2 Subjects of preliminary experiment*

Under the same quality and conditions, experiments of presenting questionnaires by sound voice or written letters and replying on display have planned twice with counterbalance of the order.

#### *4.2.3 Participants*

Students of the same university, 28 females of freshmen.

#### *4.2.4 Duration*

From January to March in 2015.

#### **4.3 Practical experiment**

#### *4.3.1 Purpose of practical experiment*

The aim of a practical experiment is to examine the validity of criteria for traits of cognitive style in terms of information processing.


5.3.3. Blockchain Impact and Value Creation

This section is an amalgamation of our identification of the bridge between prior academic research and industry reports on blockchain applications in corporate governance. This key theme was developed by identifying the characteristics, advantages and disadvantages of present and future use case applications of blockchain adoption in corporate governance through our systematic review, then linking these to value creation avenues in terms of prior academic literature and market mechanisms that are affected by blockchain adoption in corporate governance. These key factors include the following: 1. Information asymmetry—blockchain offers transparency. 2. Efficient markets—blockchain offers higher speed, efficiency, lesser agency costs and transparency. 3. Liquidity—blockchain can handle large amounts of data with ease and efficiency and more transparency. 4. Competition—blockchain allows more participation. 5. Social welfare—blockchains would result in lesser agency costs, frauds and mismanagement. 6. Agency costs—removal or reduction of agents in blockchains results in lesser agency costs. 7. Efficient asset allocation—increased speed, efficiency and transparency and more efficient markets would result in efficient resource allocation. 8. Decision-making—blockchain would result in better decision making due to lesser agency costs, more transparency and efficient markets.

#### 5.3.4. Stakeholders

This section synthesizes the stakeholders involved in blockchain adoption in corporate governance. This identification is fundamental to the understanding and implementation of blockchain adoption with regard to corporate governance. The key factors in this section include 1. firm management. 2. shareholders. 3. creditors. 4. auditors. 5. regulators. 6. investors. 7. customers and general public.

## 5.3.5. Market Mechanisms

This section identifies the market mechanisms that would be affected by blockchain adoption in corporate governance. The key factors include 1. trading, 2. short selling, 3. insider trading, 4. mergers and acquisitions, 5. initial public offerings and seasonal equity offerings, 6. executive compensation, 7. financial reporting, 8. earnings management, 9. auditing, 10. Litigation, 11. Regulation, 12. financial fraud and 13. corporate voting.

#### 5.3.6. Blockchain Governance

Finally, at the centre of the framework lies the blockchain governance section that is linked to all other sections. Without proper governance of blockchains, its adoption in any area would not be sustainable long term. Therefore, this section includes the following key blockchain governance factors: 1. blockchain protocol, 2. forks, 3. Privacy, 4. Security, 5. quality assurance, 6. Interoperability, 7. Innovation, 8. usability and efficiency and 9. cost reduction. To ensure efficient, ethical and sustainable blockchain adoption in corporate governance, it is important to understand all themes and factors identified in our DACG framework.