**3. Theoretical Framework: Decoloniality**

The article is couched in decoloniality theory. The theory has roots in Latin America with leading scholars like Walter Mignolo, Maldonado-Torres, Quijon, Dussel (Wanderley and Barros 2018) and championed by Sabelo Gatsheni Ndlovu in South Africa. It was "born out of a realisation that ours is an asymmetrical world order that is sustained not only by colonial matrices of power but also by pedagogies and epistemologies of equilibrium that continue to produce alienated Africans" (Ndlovu-Gatsheni 2013b, p. 11), who, in turn, hate, colonise, and oppress other Africans. In its broader perspective, the theory represents a commitment to challenging and reformulating the communicational scientific discourse, from a criticism of the mediating power of the Eurocentric hegemonic thinking, to a native cultural paradigm (Eliana Herrera and Del Valle Rojas 2016, p. 78). It does so through a deliberate attempt to break with monologic modernity, and issuing an invitation to engage in dialogue (Maldonado-Torres 2011, p. 261). It is, furthermore, a theory that rejects modernity, which is located on the oppressed and exploited side of the colonial difference, in favour of a decolonial liberation struggle to achieve a world beyond Eurocentric1 modernity (Grosfoguel 2011, p. 21), dehumanisation, and all acts that seek to reduce people's ontological density. The theory questions the legitimacy and sanity of celebrating postcolonial thinking, while the majority of Africans remain mentally colonised (Kaunda 2015, p. 77). The theory was chosen to inform the thinking of this article since one of the theory's primary mandates is to work towards a vision of human life that is not dependent upon or structured by the forced imposition of one ideal of society on those that differ in ideas (Mignolo 2007). The theory is appropriate in the South African contexts since it engages in a struggle against the Eurocentric design strategy [to colonise African continent], which is visible in religion informed by binarism and hierarchisation, and which keeps power imbalances and the colonial legacy intact (Sithole 2014).

It may be interesting to the reader that, in this article, decoloniality is not used to promote a struggle to decentre the Global North against Global South, as much of decoloniality research does. This article focuses on the Global South versus Global South actors, who, by default or by design, have

1 Eurocentric is a general term used in this paper to refer to what is referred to as West, or Global North, and, more precisely, the African colonisers, whose impact is still felt today, despite 26 years of South African independence.

replaced Global North colonial masters, and have occupied the big brother mentality of determining what is right and wrong, while at the same time ignoring justice and equity within the prophetic space. To this end, the article agrees with Ndlovu-Gatsheni (2013b, p. 11), who centers around the perspective that the struggle of decoloniality is a "melee against invisible vampirism of imperialism technologies and colonial matrices of power (coloniality) that continue to exist in the minds, lives, languages, dreams, imaginations, and epistemologies of modern subjects in Africa and the entire global South."

As indicated above, the article will consider the three motifs of decoloniality, namely, coloniality of power, knowledge, and critique. The article defines coloniality. Coloniality refers to long-standing patterns of power that emerged as a result of colonialism, but that define culture, labour, intersubjective relations, and knowledge production well beyond the strict limits of colonial administrations. Thus, coloniality survives colonialism (Maldonado-Torres 2011). Coloniality is a name for the 'darker side' of modernity, which needs to be unmasked because it exists as "an embedded logic that enforces control, domination, and exploitation" (Mignolo 1995). Coloniality of power is a useful concept that delves deep into the roots of the present asymmetrical global power relations, and the way the present modern world order was constituted. It boldly enables a correct naming of the current 'global political present,' as a racially hierarchised, Eurocentric, Christian-centric, patriarchal, sexist, capitalist, heteronormative, hegemonic, and modern power structure (Ndlovu-Gatsheni 2013b). Coloniality of power describes modern global power as a network of relations of exploitation, domination, and control of labour, nature and its productive resources, gender and its reproductive species, subjectivity and its material and intersubjective products, and knowledge and authority (Quiano 2007). The coloniality of knowledge poses epistemological questions that are linked to: (a) the politics of knowledge generation; (b) questions of who generates which knowledge and for what purpose; (c) the question of relevance and irrelevance of knowledge; and, (d) how some knowledges disempower/empower communities and peoples (Ake 1982; Ndlovu-Gatsheni 2013a). The article refers to coloniality of knowledge in this article mainly because prophetic movements appear to tussle and compete on issues relating to who has better and more attractive religious knowledge, in order to gain popularity. Coloniality of being is defined by Maldonado-Torres (2011) as the combination of coloniality of power and coloniality of knowledge. Furthermore, coloniality of being is that of racial invention, where the notion of race and nationality is used as the organising principle through combining power and knowledge (Sithole 2014). To this end, there exists the notion that people who are not citizens of a certain country do not belong to its zone of being; thus, they should be subjected to dehumanisation, xenophobia, and other acts that reduce their ontological density. This mentality must be challenged. In essence, the article combines these three motifs, which are pertinent and relevant to the South African context marred with coloniality manifested in many ways, in which one of them is religion.

### **4. Factors that Influence Prophetic Conflict in South Africa**

In this section, the article responds to the first question, namely, what are the factors propelling prophetic conflict in South Africa? Responding to this question will expose the readers to the challenge of prophetic conflict in South Africa. In doing so, the article intends to show the need for a Jonathanic theology, which is explained in greater depth when in response to the second question. Various factors are discussed, the first one being popularism.

Prophetic movements in South Africa have been marred by competition, which, in some cases, has gone beyond control, and has led to conflict. Religious adherents or believers move from one prophetic movement to the next, following popular prophets. Arguably, prophetic movements are the fastest growing religious movements in South Africa. While precise statistics are lacking, scholars agree that prosperity gospel followers rival, if not exceed, the numbers of followers of mainstream religions (Van Wyk 2019). This claim implies that some churches or religious groups continue to lose members to big and popular prophetic organisations. Followers of prophetic movements are not captive victims of so-called cult leaders. In fact, they move constantly between churches in search of more

efficacious "technologies" and "stronger prophets" (Van Wyk 2019). Magezi and Banda (2017) believe that the popularism sought by some contemporary prophets is a sign of them actually competing with Christ—they often project themselves as uniquely anointed by God. There is a silent force among prophets in South Africa, who are on a quest of gaining power through teaching, prophecies, and other actions. Some have required adherents eat snakes, drink diesel, be sprayed with Doom—all to gain power, which, in turn, translates into popularism. The desire for power has forced some prophets to resort to unconventional practices. In some cases, the prophets become, as suggested by Henze (1996, p. 1), legalistic and mind controlling, and they exhibit authoritarian tendencies. Cognisant of these practices, the media, politicians, prophets, and the general populace that were attracted became involved in conflict between those who practice and support what others believe to be malpractices, and those who believe that their mandate is to be harbingers of truth and the pure gospel.

Most foreign prophets have been accused of using faith to extort money from people (CRL Rights Commission 2017). In fact, during hearings of the Commission, some pastors or prophets demonstrated their resistance to the Commission by refusing to take the prescribed oath, refusing to submit the required financial documents and bank statements, and causing dramatic scenes (CRL Rights Commission 2017, p. 17). Some prophets were accused of money laundering, such as Shephard Bushiri (Lindeque 2019; TimesLIVE 2019; Evans 2019), and Pastor Alph Lukau (Montsho 2019; Matangira 2019)—both foreign nationals who operate churches in South Africa. However, according to the CRL Commission report, there are many local religious groups in South Africa who are also deemed to be "profiteering from religion," especially through their refusal to submit their financial records to the CRL commission; these include Pastor Mboro and Prophet Hadebe (CRL Rights Commission 2017). In commenting on prophet Hadebe, Collinson (2017) reports that Hadebe refused to appear before the Commission and ignored its request to view his church's financial statements.

Shepherd Bushiri, a well-known leader who had been accused of prophetpreneurship, is a Malawian who was arrested in 2018 on allegations of fraud and money laundering, among other crimes. Bushiri, in response to allegations of commercialising religion, said, "I am a businessman and that is separate from being a prophet. My prosperity is from private businesses. Such questions are not asked from leaders of white churches but when an African man prospers, then it's a problem" (Fihlani 2018).

While the article does not seek to either exonerate or condemn Bushiri, the article submits that, in South Africa, there is the idea that, when one race or nationality does something, like extort believers, it is wrong, and when it is done by another race or nationality, it becomes right. The point, which relates to the source of conflict, is that prophetic movements in South Africa, whether local or foreign, are characterised by commercialisation of religion; however, the outcry becomes louder when the movement involved has foreign origins.

The article has attempted to show that both local and foreign prophets (not only those mentioned) have been accused of profiteering, which, if it is proven to be true, means that prophetic movements must re-examine their relevance in the context of promoting social justice and social transformation. With reference to decoloniality, commercialisation of the gospel is accompanied by power, and this power makes the holder great. I, thus, problematise the commercialisation of the gospel, especially when it is used to subject the poor to even worse poverty, and to dehumanise people through criminal acts that extort money from them. Societies cannot prosper in the context of exploitation, especially by religious leaders, who are supposed to be the moral agents of change. So, in decoloniality, exploitation, whether by local or foreign prophets, must be challenged, and religious misconduct and delinquency must be prosecuted and judged—not based on race, nationality or colour—as these acts remove some people from the zone of being.

In an attempt to gain popularity, prophets have resorted to performing miracles; however, some of these miracles are questionable. One 'miracle' that became problematic was performed by Pastor Alph Lukau, and resulted in an intensification of the call for foreign prophets to return to their own countries and perform miracles for their own people (Matangira 2019; Montsho 2019). A local prophet

who seemed agitated by this miracle was Prophet Paseka 'Mboro' Motsoeneng, founder of Incredible Happenings Ministries. In responding to Lukau's so-called miracle, Jordaan (2019) cites Mboro saying, "They say I am jealous. What jealous. I gave away cars' houses. There is no competition here. I'm here because the name of Christ has been played with. This miracle is a fraud and if it's not' they must bring proof. Let's talk about it as men of God. That man never died. God will heal people when he wants' in any way he wants. Let's not fake things."

In addition, Lukau's miracle attracted the attention of Bishop Elly Mogodiri of St. Oaks Global Church of Christ, who is cited by Njilo (2019) as commenting that, "I am aware and have been duly advised that this case falls within the ambit of organised crime' fraud and misrepresentation' among other misdemeanours. As a result' I have since approached my local police station' being Hartbeespoort SAPS in the North West province' and opened an organised crime case."

It is clear from this discussion that 'fake miracles' are one of the ways in which prophets gain popularity, and also a source of conflict among the prophetic movements. From a decoloniality lens, this phenomenon refers to coloniality of power, which transfers the prophets who are deemed powerful to the zone of special being, while those prophets who cannot perform such miracles are moved from the zone of being. Decoloniality evokes the need to challenge acts and narratives that enhance people's ontological density at the expense of made-up miracles.

Criminality or mafia-based tendencies that are hidden in religious narratives are one of the common issues that haunt the prophetic movement in South Africa, and serve as a cause of conflict. Kgatle (2017) describes criminal or mafia groups as "churches [that] are known for their refusal to affiliate with established denominations in South Africa. Some of these churches also refuse to be part of the South African Council of Churches (SACC)." In most cases, these mafia churches strive, as suggested by Damiani (2002, p. 45), by "destroying any individual personality and replace the void with a cultic personality that no longer questions, thinks critically, or feels the impact of an abusive system." In most cases, this type of criminality is not visible, but is hidden from religious adherents. It causes those who are subjected to it to become incapacitated, and to see, name, describe, and explain it as it is institutionalised, naturalised, and normalised (Sithole 2014). The article agrees with the observation by Magezi and Banda (2017), that, under normal circumstances, prophets are conduits through which God's authority is extended to the believer. Sadly, this creates opportunities for prophets to abuse their followers, because the leaders are feared and considered as beyond questioning (Magezi and Banda 2017).

The list of criminality and mafia tendencies exhibited by both local and foreign prophets is long. For example, Timothy Omotoso is known for rape scandals, money laundering, and human tra fficking (Dube et al. 2017); the Seven Angels Ministry for constitutional delinquency, abuse of resources and preventing children from attending school (Dube 2019). In some cases, congregants have to ingest or are sprayed with dangerous poisons, ostensibly as part of a healing process (Jamaica Observer 2016). As long as religious leaders are guilty of such practices, religions will continue to remain a problematic institution in society, and, even worse, create competition which accelerates to conflict. Thus, Kaunda (2015, p. 75) warns that, "as long as African mind remains one of the spaces for oppressive religious structures there seems to be no possibility of academy and knowledge decolonization and consequently, no social transformation, no political progress and no economic development."

In light of these factors, evil or otherwise, the article submits that religious leaders should remain agents for peace, social justice, and accountability, through peaceful resolution of di fference, and dialogue as a benchmark that society can follow. The continued conflict, the article submits, is a "darker side" of modernity that needs to be unmasked (Mignolo 2000), because it exists as an embedded logic that enforces control, domination, and exploitation disguised in the language of salvation, progress, and modernisation (Ndlovu-Gatsheni 2013b, p. 13), and protection of citizens from foreign prophets. Cognisant of this, the article suggested the tenets of Jonathanic theology for framing a space for peaceful resolution of the conflict between local and foreign prophets.

### **5. Jonathanic Theology: What Does It Entail and What Opportunities Does It O**ff**er?**

This section responds to the second research question and discusses the themes that have been raised. Jonathanic theology is a term that was coined for the sake of this article, and for future discussions. It should not be confused with the theology propounded by Jonathan Edwards. Rather, it is framed within the story of Jonathan and David (I Samuel 18–20), a biblical passage which intends to evoke a new reading of and thinking on. The article does not intend to use the passage, like many scholars have done, to justify homosexuality or lesbianism, or to read the passage in the context of biblical wars caused by tussles for a throne. There is an attempt to reread the texts anew, informed by Ndlovu-Gatsheni's (2013a) argument that decoloniality emphasises an agenda that involves rethinking the very constitution of the present and the construction and reconstruction of new identities and philosophies. The attempt is to use the text of 1 Samuel 18–20 to tease a new theology of prophetic defence, peace, and peaceful resolution of difference in the context of ambivalence of the prophetic terrain, marked by dislodging prophetic movements with different ideas from one's own. The article wishes to emphasise that prophetic vocation can survive in the contexts of collective effort to defend oppression, racism, and inequality that arise from bolding a different theological stance. The article provides a reread and reinterpreting of 1 Samuel 18–20 informed by a decoloniality lens, especially its motifs of coloniality of power, knowledge, and being. By doing so, the theology embedded within the text will evoke a philosophy of liberation, which entails rehumanisation of the dehumanised, and the courage to care and to love (Mpofu 2017, p. 4).

### *5.1. Tenets of the Jonathanic Covenant in 1 Samuel 18–20*

This section unpacks various tenets emanating from the story of David and Jonathan. For the purposes of grounding readers on how the conceptualization of Jonathanic theology came about, the article briefly highlights the story. The story of Jonathan and David is taken from the Bible and, in particular, from 1 Samuel 18–20. Saul was the king of Israel; however, his popularity was diminishing while David was getting a spotlight among the Israelites. Saul was not pleased with David's popularism and sought to kill him. Jonathan was Saul's son and a friend to David. In the context of Saul's attack on David, Jonathan stood to protect David against his father Saul. Jonathan made a covenant with David to ensure that David was protected from Saul and, likewise, David was to remember the family of Jonathan when he ascended to the throne, which he did with Mephibosheth in 2 Samuel 9. The bottom line of the story is a commitment of Jonathan to promote peace in the context of political and religious contexts, which made me reread the story and see its applicability in the prophetic movements in South Africa, to tease new meanings of the story and debates in the field of religion, politics, and peace towards contributing to a better future for all.

While Jonathan and David were not prophets, the principles within the story can be applied into the South African prophetic space. Referring to it as Jonathanic theology does not seek to undermine the role played by David in this story, it is not the case; the article refers to Jonathanic theology because it was Jonathan who took the initiative to protect, defend, and make peace with David in the face of Saul's brutality towards David. Jonathan's actions can make up a theology on its own, especially with reference to his defence of David, and his attempts to make peace with David, regardless of Jonathan being a potential heir to the throne should David be deposed. The approach is relevant and serves as a reminder, as postulated by Abbink (2014, p. 89), that religious leaders are not only community leaders and preachers, but also moral authorities who play mediating roles in solving local conflicts—also between people of different faiths or denominations—and they should not be promoting violence and hatred of people of difference. To this end, the article identified four tenets that define Jonathanic theology for peaceful resolution of difference, namely, brotherly love greater than the throne, defence for the afflicted in society, sincerity to power, and commitment to purpose. Below, informed by decoloniality motifs, these tenets will be discussed in detail.


*Edited by Eshel Faraggi*

Since the dawn of recorded history, and probably even before, men and women have been grasping at the mechanisms by which they themselves exist. Only relatively recently, did this grasp yield anything of substance, and only within the last several decades did the proteins play a pivotal role in this existence. In this expose on the topic of protein structure some of the current issues in this scientific field are discussed. The aim is that a non-expert can gain some appreciation for the intricacies involved, and in the current state of affairs. The expert meanwhile, we hope, can gain a deeper understanding of the topic.

ISBN 978-953-51-0555-8

Protein Structure

Photo by fabiofs / iStock


**Management Strategies for Large River Floodplain Lakes Undergoing Rapid Environmental Changes**

Giri Kattel and Peter Gell *School of Science & Engineering, University of Ballarat, Victoria Australia*

#### **1. Introduction**

328 International Perspectives on Global Environmental Change

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Large river basins are the origin of ancient civilization (Barbier & Thompson, 1998; Sadoff & Grey, 2002). Floodplain lakes, situated adjacent to large river systems, are connected with river channel networks. The connectivity between river channels and wetlands makes the "boom" and "bust" ecology following the drought and flood events that continues to support diverse floral and faunal communities in the floodplains lake systems (Jenkins & Boulton, 2003). Rich biodiversity and occurrence of macro-invertebrate drifts in the Upper Paraguay River-Floodplain-System, parts of the Pantanal (Brazil) Wetland System, and dense microphyte community with regularly supplied allochthonous nutrient inputs and moderation of physical extremes in the billabongs of the Murray-Darling River Floodplain-System Australia are some examples of highly productive floodplains lake ecosystems in the world (Shiel, 1976; Wantzen et al., 2005). Being a productive ecosystem, people living across the large river basins have been greatly benefited from the resources generated by these wetlands for generations (Bright et al., 2010). For example, the indigenous people of the Orinico River Basin, South America, and Murray Darling Basin, Australia have been harvesting the specialised fish community that are adapted to the floodplains wetland systems over several centuries in the past (e.g., Lundberg et al., 1987; Humphries, 2007). Since the productivity of the large river floodplains lake ecosystems is dependent on naturally occurring riverine flood events, any alternation of the hydrological patterns of rivers can have strong impacts on nutrient dynamics, biological diversity and assemblages of these lakes (Fisher et al., 2000). Over the past few decades the large river systems and its adjacent wetland habitats have undergone rapid environmental changes. Anthropogenic activity across the river basin has increased substantially. River regulations such as construction of dams, irrigation channels, dykes and weirs, and catchment land use activities such as deforestation, agriculture and cattle ranching and introduction of exotic flora and fauna are increased (Power et al., 1996; Kingsford, 2000, Bunn & Arthington, 2002). Rapid climate warming is further intensifying the conditions of ecosystems including thechanges in hydrology and water quality of rivers and lakes (Carpenter et al., 1992; Lewis et al., 2000; Palmer et al.; 2008). The coupled human-climate disturbances have led to an increased habitat heterogeneity and complexity of ecosystem processes of majority of floodplains lake systems worldwide (Tockner et al., 2000). Consequently, the people who

Management Strategies for Large River

water aquifer.

**2. Materials and methods**

Floodplain Lakes Undergoing Rapid Environmental Changes 331

Fig. 1. Four dimensional structures of floodplain lake ecosystem (adapted after Ward, 1989). Triangular interactions (lateral-longitudinal-vertical) determine the spatial and temporal

Micro-crustaceans are one of significant indicators of rapid environmental changes of large river floodplains lakes over a range of temporal and spatial scales. Microcrustaceans prefer littoral benthic and pelagic habitats and they have wide optima and tolerance to a range of environmental variables. Micro-crustaceans such as cladocerans are one of significant components of the large river floodplains lake ecosystems. Cladocerans emerge rapidly following the inundation, feed principally on phytoplankton, bacteria and detritus and actively transfer energy across the food webs (Reid & Brooks, 2000; Jenkins & Boulton, 2003). Cladoceran exoskeletons and their ephippia are archived in floodplain lake sediment being useful indicator for a long term environmental changes (Kattel, 2011). The use of modern and sub-fossil assemblages of micro-crustaceans such as cladocerans can help floodplains lake ecologists and river scientists to understand complex ecosystem processes and develop effective management strategies for these ecosystems worldwide. In this chapter, we have identified a range of issues of rapid environmental changes of large river floodplains lake ecosystems worldwide. We have then highlighted the use of the microcrustaceans, such as cladoceran zooplankton to improve management practices of the

This chapter is based on a range of case studies on large river floodplains lake ecosystems worldwide. The case studies were varying in nature either focusing on theoretical models being developed over the past decades on large river floodplains lakes ecosystem processes, or highlighting the impacts of global environmental changes on these floodplains lake ecosystems. The theoretical models were reviewed mainly on river continuum concept

longitudinal influence is restricted in river channels. Vertical influence occurs at ground-

changes of floodplain lakes biota. Lateral influence occurs at riparian zone while

vulnerable ecosystems of floodplains lakes in the large river basins.

have been directly associated with these large river systems for a range of services over generations are influenced by these changes for sustainable living.

One of the critical issues today for majority of river scientists is therefore to understand the large river floodplains lake ecosystems processes that are exposed to a range of coupled human-climate disturbances. Understanding the ecosystem processes and identifying the disturbances altering ecosystem processes can help resource managers to tackle challenges of floodplains lake management and promote healthy and productive ecosystems across the large river basins worldwide. The large river floodplains lake ecosystems are longitudinally modulated by upstream processes, where the main source of organic carbon such as fine particulate matter is transported to downstream environments (Vannote et al., 1980). The use of particulate organic matter is maximized by benthic heterotrophs and microcrustaceans because depositional structures are limited to backwater and nearshore areas (Naiman et al., 1987). However, the role of locally derived ecological processes is unknown in the longitudinal river continuum (Statzner & Higler, 1985) since locally metabolised carbon and the bottom-up control of algal communities are also important for ecosystem processes (Wehr & Descy, 1998). Metabolism and turnover rates of organic carbon in floodplain lakes can vary with the type and nature of the river system from which they are derived. Floodplain lakes associated with blackwater rivers for example have low content of suspended sediments but a high concentration of dissolved organic matter (Meyer, 1990). Metabolism of these floodplain lakes is dependent on allochthonous organic carbon with increased river size despite increases of downstream gross primary production, where riparian swamps are the source of organic carbon (Meyer & Edwards, 1990). Flood pulses in particular are the significant source of carbon for ecosystem structure and functions in large river floodplain lakes (Junk et al., 1989). Accessibility and retention of organic matter are functions of the frequency and duration of flood pulse and extent of inundations (Humphries et al., 1999; King et al., 2003). Apart from the organic matter derived from flood pulses, integration of locally derived autochthonous matter such as phytoplankton, benthic algae and aquatic vascular plants and direct inputs from the riparian zones such as abscised leaves, particulate organic matter, and dissolved organic carbon during flood pulses are also significant source for floodplains lake ecosystem structure and function (Thorpe & Delong, 1994). Autochthonous carbon and direct allochthonous inputs from the riparian zone are labile and maximally utilized by heterotrophs dominant in near-shore leaf-litters and littoral habitats (Thorpe & Delong 1994). As the large river floodplains lakes ecosystems are exposed to coupled human-climate disturbances, scientists have been facing increasingly difficulties to understand the complex ecosystem processes worldwide.

Changes in species richness, diversity and assemblages of biota such as fish, diatoms, macro-and micro-invertebrates across temporal and spatial scales have become useful for understanding rapid environmental changes of large river floodplains lake ecosystems (Ward et al., 1999). Evaluation of the large scale changes in ecosystems as a result of microscale changes in environments such as a small rise in surface water temperature or additional inputs of phosphorous concentrations in floodplain lakes over a temporal scale is crucial in relation to changes in biotic assemblages. Interaction between the channel and floodplain systems and between the channel and groundwater aquifers plays a significant role for rapid ecosystem changes in floodplain lakes ecosystems, since the temporal dimensions of these ecosystems are largely integrated and dynamic (Fig. 1, Ward, 1989; Lewis et al., 2000).

have been directly associated with these large river systems for a range of services over

One of the critical issues today for majority of river scientists is therefore to understand the large river floodplains lake ecosystems processes that are exposed to a range of coupled human-climate disturbances. Understanding the ecosystem processes and identifying the disturbances altering ecosystem processes can help resource managers to tackle challenges of floodplains lake management and promote healthy and productive ecosystems across the large river basins worldwide. The large river floodplains lake ecosystems are longitudinally modulated by upstream processes, where the main source of organic carbon such as fine particulate matter is transported to downstream environments (Vannote et al., 1980). The use of particulate organic matter is maximized by benthic heterotrophs and microcrustaceans because depositional structures are limited to backwater and nearshore areas (Naiman et al., 1987). However, the role of locally derived ecological processes is unknown in the longitudinal river continuum (Statzner & Higler, 1985) since locally metabolised carbon and the bottom-up control of algal communities are also important for ecosystem processes (Wehr & Descy, 1998). Metabolism and turnover rates of organic carbon in floodplain lakes can vary with the type and nature of the river system from which they are derived. Floodplain lakes associated with blackwater rivers for example have low content of suspended sediments but a high concentration of dissolved organic matter (Meyer, 1990). Metabolism of these floodplain lakes is dependent on allochthonous organic carbon with increased river size despite increases of downstream gross primary production, where riparian swamps are the source of organic carbon (Meyer & Edwards, 1990). Flood pulses in particular are the significant source of carbon for ecosystem structure and functions in large river floodplain lakes (Junk et al., 1989). Accessibility and retention of organic matter are functions of the frequency and duration of flood pulse and extent of inundations (Humphries et al., 1999; King et al., 2003). Apart from the organic matter derived from flood pulses, integration of locally derived autochthonous matter such as phytoplankton, benthic algae and aquatic vascular plants and direct inputs from the riparian zones such as abscised leaves, particulate organic matter, and dissolved organic carbon during flood pulses are also significant source for floodplains lake ecosystem structure and function (Thorpe & Delong, 1994). Autochthonous carbon and direct allochthonous inputs from the riparian zone are labile and maximally utilized by heterotrophs dominant in near-shore leaf-litters and littoral habitats (Thorpe & Delong 1994). As the large river floodplains lakes ecosystems are exposed to coupled human-climate disturbances, scientists have been facing increasingly

generations are influenced by these changes for sustainable living.

difficulties to understand the complex ecosystem processes worldwide.

Lewis et al., 2000).

Changes in species richness, diversity and assemblages of biota such as fish, diatoms, macro-and micro-invertebrates across temporal and spatial scales have become useful for understanding rapid environmental changes of large river floodplains lake ecosystems (Ward et al., 1999). Evaluation of the large scale changes in ecosystems as a result of microscale changes in environments such as a small rise in surface water temperature or additional inputs of phosphorous concentrations in floodplain lakes over a temporal scale is crucial in relation to changes in biotic assemblages. Interaction between the channel and floodplain systems and between the channel and groundwater aquifers plays a significant role for rapid ecosystem changes in floodplain lakes ecosystems, since the temporal dimensions of these ecosystems are largely integrated and dynamic (Fig. 1, Ward, 1989;

Fig. 1. Four dimensional structures of floodplain lake ecosystem (adapted after Ward, 1989). Triangular interactions (lateral-longitudinal-vertical) determine the spatial and temporal changes of floodplain lakes biota. Lateral influence occurs at riparian zone while longitudinal influence is restricted in river channels. Vertical influence occurs at groundwater aquifer.

Micro-crustaceans are one of significant indicators of rapid environmental changes of large river floodplains lakes over a range of temporal and spatial scales. Microcrustaceans prefer littoral benthic and pelagic habitats and they have wide optima and tolerance to a range of environmental variables. Micro-crustaceans such as cladocerans are one of significant components of the large river floodplains lake ecosystems. Cladocerans emerge rapidly following the inundation, feed principally on phytoplankton, bacteria and detritus and actively transfer energy across the food webs (Reid & Brooks, 2000; Jenkins & Boulton, 2003). Cladoceran exoskeletons and their ephippia are archived in floodplain lake sediment being useful indicator for a long term environmental changes (Kattel, 2011). The use of modern and sub-fossil assemblages of micro-crustaceans such as cladocerans can help floodplains lake ecologists and river scientists to understand complex ecosystem processes and develop effective management strategies for these ecosystems worldwide. In this chapter, we have identified a range of issues of rapid environmental changes of large river floodplains lake ecosystems worldwide. We have then highlighted the use of the microcrustaceans, such as cladoceran zooplankton to improve management practices of the vulnerable ecosystems of floodplains lakes in the large river basins.

## **2. Materials and methods**

This chapter is based on a range of case studies on large river floodplains lake ecosystems worldwide. The case studies were varying in nature either focusing on theoretical models being developed over the past decades on large river floodplains lakes ecosystem processes, or highlighting the impacts of global environmental changes on these floodplains lake ecosystems. The theoretical models were reviewed mainly on river continuum concept

Management Strategies for Large River

water volume (King et al., 2003).

Floodplain Lakes Undergoing Rapid Environmental Changes 333

Fig. 2. Free flowing rivers consist of series of rapids and slow flowing stretches. Rapids are important for succession of food web structure and dynamics. Naturally occurring physical structures enhance water quality, energy budget and flow regime of the river. Improved water quality maintains the health of riverine floodplain lake ecosystems. In impounded rivers, rapids have been removed by erected dams. Dams can have direct implications on hydrology reducing the downstream flow variation. Dams hinder the upstream migration of biota, alter thermal environment, nutrient movement and sediment loading and predatorprey interaction in downstream food webs (adapted after Nilsson & Berggren, 2000).

Regulation of the Murray River, Australia over the past 50 years has resulted in considerable implications for ecosystem structure and functions. For example, construction of dams in the Murray River has reduced downstream flows as well as obstructed upstream migration of biota including thermal environment, nutrient movement and sediment loading and predator-prey interactions (Gehrke & Harris, 2000). Since flooding generates biogeochemical processes, the major impact of dams is the interruption of the exchange of energy between river and riparian zone during flood events (Sam et al., 2000). Low flows events are critical for lowland fish assemblages and plant community structure (Capon, 2003). Increased water residence time increases crustacean biomass (Humphries et al., 1999). However, alternation of natural low flow patterns can influence diadromous fish populations which utilize crustaceans as their major diet. Fish species such as Murray cod (*Maccullochella peelii peelii*) and silver perch (*Bidyanus bidyanus)* which do not require special flood events in the Murray River Australia is able to utilize low-flows events for spawning (King et al., 2003. However, the growth of larvae of Murray cod (*M. peelii peelii)* is significantly influenced by construction of dams and irrigation channels across the MDB. Larvae are consistently stranding in the dam when drifting (Koehn & Harrington, 2005). In contrast, recruitment of other fish species requires floodplain inundation and increased

Alteration of riparian vegetation can influence nutrient sources of wetland biota. Composition and diversity of naturally occurring riparian forests such as river red gum trees (*Eucalyptus camaldulensis*) in MDB have declined as a result of river regulation (Robertson et al., 2001). For example, stable isotope ratios of oxygen reveal that river red gum (*E. camaldulensis*) forests are efficient for utilizing water at varying salinity gradients (Mensforth et al., 1994) through reduced transpiration rates (Costelloe et al., 2008). However, continued low flows occur as a result of a rise in ground water salinity. Absence of natural floods influences recharge of naturally occurring groundwater salinity levels and will have detrimental effects on floodplains riparian biota (Jolly et al., 2001). Die back in

(RCC), flood pulse and riverine productivity (RPM) models, where most of these models were tested in North America, Europe and Australia for understanding ecosystem processes of the large river systems (Vannote et al., 1980; Naiman et al., 1987; Junk et al., 1989; Thorpe & Delong, 1994). The case studies on critical management issues of rapid environmental changes of the large river basins were collated from various continents including the Yangtze River System, Asia (Yang et al., 2007; Chen et al., 2011), the Mississippi River System, North America (Wren et al., 2008), the Orinco, Salado and the Paraguay River Systems, South America (Lundberg et al., 1987; Claps et al., 2009), Orange-Vaal River System, South Africa (Ashton et al., 1986), Erbo River System, Europe (Gallardo et al., 2007) and the Murray Darling River System, Australia (Humphries et al., 1999; King et al., 2003). Following the identification of critical management issues of the large river systems, prevailing conditions of biotic assemblages in changes in large river floodplain lakes were reviewed from the case studies of Europe, North America and Australia (Fisher et al., 2000; Lewis et al., 2000). Then a comprehensive review was undertaken on the use of microcrustaceans to understand the complex ecosystem processes and configure effective management strategies when they are exposed to a range of external disturbances including climate change over temporal and spatial scales.


*Proceeding Paper* **Detection and Classification of Power Quality Disturbances Using LSTM †**

**Fatma Zohra Dekhandji \*, Abdelmadjid Recioui, Athmane Ladada and Taha Slimane Moulay Brahim**

Laboratory of Signals and systems, Institute of Electrical and Electronic Engineering, University M'hamed Bougara of Boumerdes, Boumerdes 35000, Algeria

**\*** Correspondence<EMAIL_ADDRESS>
† Presented at the 2nd International Conference on Computational Engineering and Intelligent Systems, Online, 18–20 November 2022.

**Abstract:** The detection and diagnosis of power quality (PQ) issues are critical topics in the electrical power system's generation, transmission, and distribution. Nonlinear loads, power electronic converters, system malfunctions, and switching events are the most common causes of PQ issues. The main purpose of this work is to use an artificial intelligence (AI) technique based on automatic feature extraction to discover and identify PQ difficulties. The AI technique consists of a dedicated architecture of the Long Short-Term Memory (LSTM) network, which is a special type of Recurrent Neural Network (RNN).

**Keywords:** smart grids; power quality; monitoring; LSTM

#### **1. Introduction**

In today's world, power quality (PQ) is important for modern electric power utilities and their consumers, especially when it comes to power quality disturbances, because of the huge increase in nonlinear load, the increased use of sensitive electronic devices, and the need to apply green power globally as well as increase renewable energy applications. Power quality issues have become more crucial than ever [1]. Interruptions and other disturbances are caused by greater usage of semiconductor devices, lighting controls, solidstate switching devices, inverters, and protection and relaying equipment. Such issues have become some of the most pressing concerns for engineers and decision-makers, as frequent occurrences result in significant financial losses for power companies [2].

Detecting power quality issues is critical for improving power quality in electric power systems and for making effective decisions about how to handle network disturbances. Engineers reading waveforms in the field is one method of identifying and classifying power disruptions. Unfortunately, due to the amount of data sampled in current power systems, manual recording is nearly impossible. Furthermore, more equipment failures in power systems occurred because of PQ problems, as well as damage to its sensitive controllers. As a result, the expense of addressing these issues is high in terms of finances, and it results in time losses. Therefore, studying the waveforms of power quality disturbances is important in order to detect and classify them successfully [3]. The power system must overcome various power quality challenges; some of them make it operational, resulting in a blackout of the network. The most significant challenges that the power system must overcome include:voltage spikes, voltage fluctuations, voltage unbalance, voltage sag, voltages swell, harmonic distortion, noise, very short interruptions, and long interruptions [4].

In order to evaluate the power quality and the disturbances that are experienced, it is necessary to detect and classify these disturbances in time, precisely and correctly. To do that, several signal processing methods, such as FT, ST, and WT, have offered sophisticated mathematical algorithms that may detect power quality problems, and these algorithms have shown substantial success in this field [3–5]. Classification techniques like ANN, SVM,

**Citation:** Dekhandji, F.Z.; Recioui, A.; Ladada, A.; Moulay Brahim, T.S. Detection and Classification of Power Quality Disturbances Using LSTM. *Eng. Proc.* **2023**, *29*, 2. https:// doi.org/10.3390/engproc2023029002

Academic Editor: Hamid Bentarzi

Published: 10 January 2023

**Copyright:** © 2023 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/).

and Fuzzy logic provide assistance to the detecting algorithm by testing, validating, and training large amounts of signals. This helps to ensure that the selected algorithm is both successful and efficient [6]. Long Short-Term Memory (LSTM) hasprovided a powerful mathematical algorithm for the detection and classification of PQ problems in a power system. LSTM isa developed version of Recurrent Neural Network (RNN). LSTM contains neurons to perform computation (memory cells). These cells have weights and gates; the gates are the defining characteristic of LSTM models. There are three gates within each cell: theinput, forget, and output gates [7].

In this work, the PQ detection and classification system is presented in order to solve part of the power system problems. The paper is divided into the following sections: a description of power quality problems with their causes and effects. Next, a presentation of the existing detection and classification methods is given, with a brief explanation oftheiradvantages and drawbacks. Finally, the simulation results and their discussion are given, with conclusions drawn.

#### **2. Long Short-Term Memory (LSTM)**

#### *2.1. Recurrent Neural Networks (RNN)*

Recurrent neural networks (RNNs) are networks with an internal network loop that ensures information persistence. RNNs have been particularly developed to process sequential data [8]. RNNs have complete connections between adjacent layers and nodes within the same layer as illustrated in Figure 1. Furthermore, RNNs' hidden units receive feedback from prior states to current status. These properties are appropriate for dealing with temporal–spatial data [9].

**Figure 1.** Architecture of a recurrent neural network.

RNNs are referred to as recurrent because they do the same task for each element of a sequence, with the outcome relying on previous estimates. Another way to conceive of RNNs is that they have a "memory" that stores information about previous calculations [10]. As a result, RNNs can link earlier data to the current task. RNN is already being used to solve a number of problems using an internal state (memory) to process a sequence of inputs, including language modeling and translation, speech recognition, text recognition, time series data, and autonomous driving systems to predict vehicle trajectories and assist in avoiding accidents.

#### *2.2. RNN Architecture*

In Figure 2, (xt) denotes the current input of time step t, (ht) is the output of that time step, and A denotes the network's recurrently connected unit. The previous time step's output is passed on to the following time step as an extra input. If a dense layer is following, the last concealed state (ht) is passed and processed to form (yt ), or if a dense layer follows, (ht) is passed and processed to form (yt ).

**Figure 2.** A simple rolled RNN layer and its corresponding unrolled architecture.

#### *2.3. Long Short-Term Memory (LSTM)*

Long Short-Term Memory networks are a developed version of the previously mentioned simple RNNs. Hochreiter and Schmidthuber first proposed them in 1997 [11], and they have demonstrated accurate performance in modeling both long- and short-term dependencies of sequential data [8]. In theory, they were created expressly for long-term dependencies in order to overcome the vanishing/exploding gradient problem. Figure 3 shows the structure of a simple LSTM network for prediction, which includes a sequence input layer, an LSTM layer, and a prediction (classification) output layer.

**Figure 3.** LSTM network system.

#### *2.4. LSTM Network Architecture*

The LSTM architecture is based on the existence of a memory cell that is capable of maintaining its state over time and has a nonlinear gating mechanism that controls the flow of information into and out of the cell. The LSTM network is being utilized to create a more complicated and deeper nonlinear neural network capable of demonstrating the effect of long-term memory [12].

LSTM networks have an input layer, an output layer, and a multitude of hidden layers in between. The memory cell is built into the hidden layer. Each cell has three gates (input, forget, and output) and a recurrent connection unit [13].

The LSTM architecture is depicted in Figure 4 as it unfolds over time. The LSTM cell (denoted as A in Figure 4) on the left represents the previous time step, while the cell on the right represents the next time step. The current time step is halfway through. The cell contains three lines. It obtains the input (Xt) and the output from the previous time step in the bottom left corner (the output from the previous layer is called the hidden state in RNNs and is abbreviated as (ht−1). Before running into the four gates denoted as yellow boxes in the drawing, the input (Xt) and the hidden state (ht−1) are concatenated. The cell's third input, which comes from the preceding cell, is represented by a straight arrow that passes through the upper section of the cells. This is the cell state, which allows the LSTM to remember long-term dependencies with a far lower risk of the vanishing and exploding gradient problems that plague typical RNNs [14–17].

**Figure 4.** LSTM module containing four interacting layers.

#### **3. Results and Discussions**

#### *3.1. Description of the Simulation Environment*

MATLAB is used to generate several PQ signals, including interruptions, sag, harmonic distortion, flicker, swell, surge, sag with harmonics, and swell with harmonics, in this section. A comparative study to evaluate the LSTM performance in terms of the detection and classification of these signals is performed. Each simulated waveform is made up of voltage waves recorded at a rate of 64 samples per cycle. A total of 200 case studies are created for each power quality problem by varying the voltage magnitude and the beginning and finish time instants of each PQ problem. Simulated signals are blended with random white noise with signal-to-noise ratios of 40 dB and 20 dB, respectively.

The signal-to-noise ratio (SNR) is a metric that compares the strength of a desired signal to the strength of an additive white Gaussian noise as:

$$\text{SNR} = \frac{\text{Psignal}}{\text{Proise}} \tag{1}$$

where P designates the power. Higher values of SNR generally mean a better specification since this means more useful information (the signal) than unwanted data (the noise).

#### *3.2. Proposed Methodology*

As shown in Figure 5, the LSTM system is designed and built to perform detection and classification using five parameters: amplitude, start time, end time, duration, and THD percent, as well as two outputs: waveform class and harmonics indicator.

**Figure 5.** Block diagram of proposed method.

#### *3.3. Model Implementation and Training*

In this section, the proposed model is trained by altering the voltage magnitude and the start and finish time instants of each power quality problem; 200 case studies are constructed for each PQ problem. Figure 6 shows the trained system.

**Figure 6.** Training process of LSTM model.

To detect and classify the problem waveforms, the LSTM has two outputs as follows:

**Table 1.** LSTM Network Outputs.

#### *3.4. Results and Discussions*

Table 2 summarizes the performance of the LSTM technique following extensive simulations. The percentage values indicated show how good LSTM is in terms of power quality disturbance detection and classification.

**Table 2.** Summary of the Obtained results.

The following remarks may be drawn from Table 2:

#### **4. Conclusions**

The main objective of this work is to develop a Power Quality Detection and Classification using Long Short-Term Memory (LSTM). The method is able to identify and classify several PQ problems (normal, sag, swell, surge, distortion, and interruption) in both simple and complex power quality disturbances in the presence of random noise at different values of the signal-to-noise ratio (SNR). The detection and classification using the LSTM model aredesigned and implemented to perform with five input data parameters (amplitude, start time, end time, duration, THD %) and two outcomes: the waveform class and a harmonics content indicator.The detection and classification of PQ problems using LSTM show the high accuracy of this model, even in the worst cases of adding harmonics with 30-dB SNR simultaneously with PQ problemsignals. This proves the robustness of the LSTM method, as the obtained classification rate was 96.3674%. Hence, the findings of the present work prove that the LSTM network is a leading method in the world of PQ detection and classification.

**Author Contributions:** Conceptualization, F.Z.D. and A.R.; methodology, A.R.; software, F.Z.D.; validation, A.R., T.S.M.B. and A.L.; formal analysis, F.Z.D.; investigation, A.R.; resources, F.Z.D.; data curation, T.S.M.B.; writing—original draft preparation, F.Z.D.; writing—review and editing, A.R.; visualization, F.Z.D.; supervision, A.R.; project administration, F.Z.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data have been synthesized for the purpose of this study and are not publicly available.

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

#### **References**

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*4.2. Configuring Voting Classifiers*

There is a wide range of single and ensemble ML algorithms that voting classifiers can use for a given prediction. This study explores different single and ensemble ML methods used in the literature to reach the best combination for the given study (Figure 11).

**Figure 11.** Configuration of soft and hard voting classifiers.

This study aimed at developing a COR impact predictor that achieves good prediction accuracy with simple implementation. Therefore, dimension reduction, class imbalances, and optimization techniques were not used to ascertain the best hyperparameters. Instead, the layers of different ML approaches were placed within a voting classifier and, based on the observed performance, a final voting classifier was configured with fewer ML members to accelerate the training procedure. The compulsory parameters, including the number of estimators (for RF, AB, and GB) and the number of neighbors (for KNN), were set roughly close to the benchmark models, while the other options were left as default. Although feature engineering and optimization techniques would have improved the performance of the ensemble predictor, their implementation was beyond the scope of the present study.

The radial basis function (RBF) was used as the SVM kernel while enabling a balanced class weight option. KNN was trained with 23 neighbors. The LR was adjusted with a LIBLINEAR optimizer and an l1 regularization. To ensure the creation of a weak learner, NB was used in its default form. The number of estimators for AB and RF was adjusted to 300 trees. The GB used 100 estimators while its learning rate was customized at 0.1. Afterwards, in order to reduce computational cost, the number of voting classifier members was reduced to three. In this respect, two strong learners were combined with a weak learner to simultaneously ensure accuracy and the elimination of bias. Combining strong and weak ML learners enhances the prediction accuracy of models for different rework cost impact classes while boosting the model's overall performance in terms of generalization ability and computational cost. Therefore, LR and KNN were used as strong learners, while NB was used as a weak learner for both the soft and hard voting classifiers.

#### *4.3. Configuring Benchmark Classifiers*

Unlike the voting classifiers, which were configured without any particular attention to the fine-tuning of their hyperparameters, each of the benchmark ML approaches was specifically fine-tuned to ensure a fair comparison between the ensemble voting classifiers and single ML predictions (Table 1).

**Table 1.** Hyperparameters of benchmark COR classifiers.

#### *4.4. Evaluation Metrics*

The prediction performance of the OCR impact predictors was evaluated using conventional accuracy, precision, accuracy, and F1 scores. For this, the number of correct predictions of each cost class (true positive (TP)) and correct assignments of the sample to the rest of the subclasses (true negative (TN)) was obtained. Likewise, the incorrect predictions within each subclass (false positive (FP), and false negative (FN)) were also recorded. Accuracy, F1 scores, precision, and recall [73] were obtained using Equations (2)–(5).

$$Accuracy = \frac{TP + TN}{TP + TN + FP + FN} \tag{2}$$

$$F1\ \text{score} = \frac{2 \times (Precision \times Recall)}{Precision + Recall} \tag{3}$$

$$Precision\_{Multiclass} = \frac{\sum\_{i=2}^{classes} TP\_i}{\sum\_{i=2}^{classes} TP\_i + FP\_i} \tag{4}$$

$$Recall\_{Multiclass} = \frac{\sum\_{i=2}^{classes} TP\_i}{\sum\_{i=2}^{classes} TP\_i + FN\_i} \tag{5}$$

Accuracy did not provide a satisfactory evaluation for an imbalanced dataset as it does not consider FP and FN. Thus, the F1 score was preferred for the imbalanced dataset as it combines precision and recall. However, for a reliable COR predictor, the accuracy of the subclasses also needed to be evaluated.

#### **5. Results and Discussion**

The prediction performance of the soft and hard voting classifiers along with the benchmark ML approaches are shown in Table 2.

**Table 2.** COR prediction performance results.

As Table 2 shows, LR outperformed all predictors in terms of F1 score for the fivelevel cost impact prediction. Additionally, DT and GB displayed better F1 scores for the three- and four-level COR impact predictions. However, the accuracy and F1 scores can be misleading when working with an imbalanced dataset, so to investigate the practicality of the predictors, their ability in predicting each cost impact class needs to be investigated. This is best achieved by measuring the F1 score of each of the cost impact classes. Figure 12 presents the prediction performances of the best-performing classifiers within the benchmark model with the hard and soft voting classifiers.

As Figure 12 shows, both the soft and the hard voting classifiers were able to detect the high-cost impact items with only a 4% occurrence (support = 23). Among the COR classifiers, however, only one soft voting classifier detected the rework with a cost impact of five (VH) with a low occurrence of 1% (support = five).

A high accuracy with DT was only associated with nonconformance items with a very low cost impact, and it performed poorly for other underrepresented but more important cost impact classes (Figure 13). Likewise, voting classifiers exhibited better performance for medium- and high-impact cost estimation compared with GB, which completely failed to predict COR with a medium impact (Figure 14). Despite its poor class performance in the four-level classification, RF resulted in the best prediction for high-impact COR items without sacrificing overall accuracy. On the other hand, the soft voting classifier proved to be consistent in its precision accuracy for different classification levels.

**Figure 14.** Prediction performance in 3-level classifiers.

To achieve a robust COR predictor, it is important to evaluate the ability of ML to solve the problem. Most single and tree-based ensemble ML predictors failed to estimate COR with high (four) or very high (five) impacts. The superior accuracy of benchmark ML approaches is due to their predicting low-cost impact rework items. However, they are incapable of predicting high-impact cost items. The prediction of low-cost impact rework items cannot reduce the deviation between the as-planned and as-built costs. On the other hand, voting classifiers are more successful in predicting high-impact COR items. The soft voting classifiers were the most robust for COR, displaying a significantly good performance in detecting underrepresented cost impact classes.

To better illustrate the practical implementation of the model, a trained soft voting classifier was used to predict an unseen user input from the test dataset. For example, the user may want to evaluate the material-related nonconformities of a high-rise building project using the available construction team status report and site conditions. The user defines a scenario in which negligence in the initial material inspection due to a lack of site supervision (O-4) results in the receipt of defective material (M-2) from the supplier. The defective material, accompanied by an insufficient review of the design documents (C-15), causes a deviation from the design (C-3). Thus, with respect to the scenario specified by the user, the system can predict the cost impacts of different construction activities under a user-defined scenario. Once the user specifies different activities, such as the facade works (ceramic, coating, insulation, etc.), the system uses the trained soft voting classifier to evaluate its impact on the overall construction cost. In this example, the soft voting classifier can accurately predict a cost impact of three (medium) on the overall construction cost.


**1. Introduction**

### **1.1 Background**

Rice (*Oryza sativa*) is the staple food of an estimated 3.5 billion people from East and South Asia, the Middle East, the West Indies, and Latin America, accounting for 50–80% of their daily calorie intake [1]. It is also the primary source of income and

employment for more than 200 million households in developing countries [2]. Rice is the main staple providing over 45% of the total caloric and 40% of the protein requirements for the Sri Lankan population [3, 4]. In the country, rice cultivation occupies 16% of the total land area and 34% of the total cultivated area whereby 11.6% of the total local population and 32% of the total labor force are directly engaged in the rice sector [5]. The rice sector contributes 7% to the national agricultural GDP [6], and therefore is associated with food and nutrition security and is critical for generating livelihood income for the Sri Lankan population.

Salinization of arable soils is an increasing challenge in global agriculture. Worldwide about 20% of total cultivated and about 33% of irrigated agricultural lands are afflicted due to soil salinity [7, 8]. Soil salinization is aggravated by the adverse effects of climate change causing abandonment globally of 0.3–1.5 million hectare year1 [9]. Consequently, an estimated 50% of the arable land would be salinized by the year 2050 [10]. The cost of global crop loss owing to salinization is estimated to be USD 27.3 billion [11]. Rice, with a threshold of 3 dSm1 for most cultivated varieties, is very sensitive to salinity [12], and rice cultivation is extremely vulnerable to soil salinization. In moderately saline areas, the yield loss of rice is 10–15%, whereas in highly saline areas, the yield was reduced by 30–45% [13]. Soil salinity has become a major limiting factor for local rice production, especially in the irrigated farming systems in the north-central and eastern plains, and in the rain-fed systems in the west coast and the Jaffna peninsula [14]. The net income of rice production is reduced up to 22% and 43% in moderate and highly saline areas, respectively in Sri Lanka [15], and therefore salinity has a significant impact on the economic sustainability of local rice production systems.

### **1.2 The concept of soil salinity**

Saline soils have an excess accumulation of Na+ , K+ , Ca2+, Mg2+, HCO3 , Cl, NO3 , SO4 2 , and CO3 2 or mixtures of these ions [16], thereby affecting the normal functions of plant growth. Based on the physiochemical properties: electrical conductivity (EC), exchangeable sodium percentage (ESP), sodium adsorption ratio (SAR), and pH, saline soils can be categorized as saline, sodic (alkali), and salinesodic soils. Optimal soil conditions for crop growth are EC <4 dSm1 , pH = 6.5–7.0, SAR <13, and ESP < 13 [17]. Saline soils are characterized by EC of saturated extract >4 dSm1 , pH <8.5, SAR <13%, and ESP <15% of the exchangeable cations. However, saline soils often are in normal physical conditions with good structure and permeability, and therefore with proper management measures can be used for crop cultivation. In contrast, sodic soils are low in total salts but are characterized by high exchangeable Na+ . Sodic soils record high ESP >15%, pH > 8.5, and SAR >13%, but EC is often <4 dSm1 [18, 19]. High levels of sodium and low total salts result in dispersed soil particles, and poor physical properties and sodic soils are sticky when wet, but hard, cloddy, crusty, and nearly impermeable to water when dry. Salinesodic soils contain large amounts of total soluble salts but greater than 15% of exchangeable Na+ . The pH is <8.5, SAR >15%, and the EC is >4 dSm1 . The physical properties of saline-sodic soils are good as long as an excess of soluble salts is present.

Soil salinity can arise due to natural processes causing primary salinity or due to manmade secondary salinity. In field conditions, low salinity *viz.* EC 2–4 dSm1 can arise from natural salinity and/or irrigation salinity. Species with low to moderate salt tolerance can be grown successfully in low saline soils. Moderate to high salt*The Scale and Complexity of Salinity Impacts on Sri Lankan Rice Farming Systems:… DOI: http://dx.doi.org/10.5772/intechopen.112651*

tolerant plant species can be grown when the EC is between 4 and 8 dSm1 [20] usually present in water logged irrigated conditions. Under high salinity with an EC value of >9 dSm1 only halophytes can be grown [21], and therefore the choice of crops is a useful measure of salinity management. Rice expresses low tolerance and based on current guidelines rice yields decrease by 12% for every unit of (dSm1 ) salinity increase above 3.0 dSm1 [22, 23]. Detailed analysis of the salinity problem including associated intricate mechanisms, therefore, can help in developing effective management practices including the selection of tolerant rice varieties.

#### **1.3 Major factors underlying salinity tolerance**

Salinity tolerance in plants involves a number of traits that act in isolation (independent) or in combination (less independent) [24, 25]. The adverse effects of salinity on plant growth are generally associated with the osmotic potential of the soil solution and the high level of toxicity of sodium (and chloride for some species) that causes multiple disturbances in crop metabolism, growth, and development at the molecular, biochemical and physiological levels [26]. Plant response to salinity is expressed in two major phases: the initial, rapid osmotic phase that inhibits growth and, a later, slower ionic phase that accelerates tissue and organ senescence [27]. Salinity-induced osmotic effects reduce plant biomass and yields; however, selected ions, such as Na+ , Cl, Ca+2, and Ba+ , cause additional injury and crop damage [28]. Accordingly, three distinct salinity response mechanisms were described in tolerant germplasm, including osmotic stress tolerance, Na+ exclusion from photosynthetic and other sensitive tissues, and tissue tolerance against accumulated Na+ and possibly also accumulated Cl [27].

Rice is relatively tolerant to salinity at germination and late vegetative growth, compared to the early seedling stage (3-leaf stage) and reproductive stage (pollination and fertilization). Therefore, apparently, plants at different developmental stages may express one or more of the different tolerance mechanisms. However, there is a poor correlation between the tolerance mechanisms expressed at the two most salt-sensitive stages, the early seedling and reproductive stages [27]. Multiple tolerance phenotypes, associated mechanisms, and genes were identified from different salinity-tolerant germplasm at different development stages [3, 29]. Therefore, a thorough understanding of the molecular mechanisms associated with tolerance traits, and advanced technological innovations to incorporate the traits into elite varieties can accelerate breeding programs targeting saline tolerance.

This chapter aims to review the literature available on the nature and the scale of soil salinization problems in local rice farming environments. The analysis, thereby, would provide actionable insights and potential targets to reduce the genetic vulnerability of elite germplasm and to improve soil salinity management strategies in local rice farming and production environments.