Susceptibility mapping of groundwater salinity using machine learning models.

Susceptibility mapping of groundwater salinity using machine learning models. Environ Sci Pollut Res Int. 2020 Oct 25;: Authors: Mosavi A, Sajedi Hosseini F, Choubin B, Taromideh F, Ghodsi M, Nazari B, Dineva AA Abstract Increasing groundwater salinity has recently raised severe environmental and health concerns around the world. Advancement of the novel methods for spatial salinity modeling and prediction would be essential for effective management of the resources and planning mitigation policies. The current research presents the application of machine learning (ML) models in groundwater salinity mapping based on the dichotomous predictions. The groundwater salinity is predicted using the essential factors (i.e., identified by the simulated annealing feature selection methodology) through k-fold cross-validation methodology. Six ML models, namely, flexible discriminant analysis (FDA), mixture discriminant analysis (MAD), boosted regression tree (BRT), multivariate adaptive regression spline (MARS), random forest (RF), support vector machine (SVM), were employed to groundwater salinity mapping. The results of the modeling indicated that the SVM model had superior performance than other models. Variables of soil order, groundwater withdrawal, precipitation, land use, and elevation had the most contribute to groundwater salinity mapping. Results highlighted that the southern parts of the region and some parts in the north, northeast,...
Source: Environmental Science and Pollution Research International - Category: Environmental Health Authors: Tags: Environ Sci Pollut Res Int Source Type: research