A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of classifying sites to obtain a proxy site for a site of interest, the method comprising: determining critical parameters among parameters estimated by one or more models associated with each of the sites, the parameters including the critical parameters, a parameter of interest, and other parameters, and the critical parameters being determined to be critical in estimation of the parameter of interest according to the one or more models; grouping two or more sites together in a same group when the two or more sites have same critical parameters; for each group of the two or more sites, classifying the two or more sites by type; correlating the type associated with each site with latitude, longitude, and elevation of the site; and obtaining, using a processor, the proxy site for the site of interest by determining the type correlated with latitude, longitude, and elevation of the site of interest. 2. The method according to claim 1, wherein the classifying the two or more sites in each group by type includes associating each of the two or more sites with a set of polynomial coefficients. 3. The method according to claim 2, wherein the classifying each of the two or more sites of each group into a type includes using an unsupervised clustering machine learning algorithm based on the set of polynomial coefficients associated with each of the two or more sites of each group. 4. The method according to claim 3, wherein the classifying includes receiving an indication of a number of the types. 5. The method according to claim 2, further comprising obtaining the set of polynomial coefficients associated with each of the two or more sites of each group by fitting polynomial models to first and second order dependence of error in estimation of the parameter of interest on each of the critical parameters associated with each of the two or more sites. 6. The method according to claim 2, further comprising obtaining the set of polynomial coefficients associated with each of the two or more sites of each group by fitting a linear model of the parameter of interest to the critical parameters associated with each of the two or more sites. 7. The method according to claim 1, wherein the correlating includes training a supervised classification machine learning model. 8. A system to classify sites to obtain a proxy site for a site of interest, the system comprising: an input interface configured to receive an input of a number of types; and a processor configured to determine critical parameters among parameters estimated by one or more models associated with each of the sites, the parameters including the critical parameters, a parameter of interest, and other parameters, and the critical parameters being determined to be critical in estimation of the parameter of interest according to the one or more models, to group two or more sites together in a same group when the two or more sites have same critical parameters, to classify the two or more sites of each group into a type, the number of types being specified in the input, to correlate the type associated with each site with latitude, longitude, and elevation of the site, and to obtain a proxy site for the site of interest by determining the type correlated with longitude, latitude, and elevation of the site of interest. 9. The system according to claim 8, wherein the processor classifies the two or more sites of each group into the type based on associating each of the two or more sites with a set of polynomial coefficients. 10. The system according to claim 9, wherein the processor uses an unsupervised clustering machine learning algorithm based on the set of polynomial coefficients associated with each of the two or more sites of each group to classify each of the sites. 11. The system according to claim 9, wherein the processor obtains the set of polynomial coefficients associated with each of the two or more sites of each group by fitting polynomial models to first and second order dependence of error in estimation of the parameter of interest on each of the critical parameters associated with each of the two or more sites. 12. The system according to claim 9, wherein the processor obtains the set of polynomial coefficients associated with each of the two or more sites of each group by fitting a linear model of the parameter of interest to the critical parameters associated with each of the two or more sites. 13. The system according to claim 8, wherein the processor correlates the type associated with each site with latitude, longitude, and elevation of the site based on training a supervised classification machine learning model. 14. A non-transitory computer program product having computer readable instructions stored thereon which, when executed by a processor, cause the processor to implement a method of classifying sites to obtain a proxy site for a site of interest, the method comprising: determining critical parameters among parameters estimated by one or more models associated with each of the sites, the parameters including the critical parameters, a parameter of interest, and other parameters, and the critical parameters being determined to be critical in estimation of the parameter of interest according to the one or more models; grouping two or more sites together in a same group when the two or more sites have same critical parameters; for each group of the two or more sites, classifying the two or more sites by type; correlating the type associated with each site with latitude, longitude, and elevation of the site; and obtaining the proxy site for the site of interest by determining the type correlated with latitude, longitude, and elevation of the site of interest. 15. The non-transitory computer program product according to claim 1, wherein the classifying the two or more sites in each group by type includes associating each of the two or more sites with a set of polynomial coefficients. 16. The non-transitory computer program product according to claim 15, wherein the classifying each of the two or more sites of each group into a type includes using an unsupervised clustering machine learning algorithm based on the set of polynomial coefficients associated with each of the two or more sites of each group. 17. The non-transitory computer program product according to claim 16, wherein the classifying includes receiving an indication of a number of the types. 18. The non-transitory computer program product according to claim 15, further comprising obtaining the set of polynomial coefficients associated with each of the two or more sites of each group by fitting polynomial models to first and second order dependence of error in estimation of the parameter of interest on each of the critical parameters associated with each of the two or more sites. 19. The non-transitory computer program product according to claim 15, further comprising obtaining the set of polynomial coefficients associated with each of the two or more sites of each group by fitting a linear model of the parameter of interest to the critical parameters associated with each of the two or more sites. 20. The non-transitory computer program product according to claim 14, wherein the correlating includes training a supervised classification machine learning model.