Application of machine learning techniques in groundwater potential mapping in the Korba coastal aquifer Cap Bon Peninsula

Abstract

Groundwater potential mapping (GWPM) in the coastal areas is central for the decion making and development of the society and the environment. The current study endeavours the delineation of the groundwater potential zones of Korba coastal aquifer in the Cap-Bon Peninsula in the North East of Tunisia, using three different machine Learning techniques : random forest (RF), boosted regression tree (BRT), and the ensemble of RF and support vector machine (SVM). In order to achieve the objective, 17 groundwater influencing factors including elevation, slope, aspect, slope length (LS), profile curvature, plan curvature, topographical wetness index (TWI), distance from streams, distance from lineaments, lithology, geomorphology, soil, land use, normalized difference vegetation index (NDVI),Stream Power Index,Drainage Density and rainfall were considered for inter-thematic correlations and overlaid with wells locations and Transmissivity data in a spatial database. A total of 225 wells locations were identified, which had been divided into two classes: training and validation, at the ratio of 70:30, respectively. The RF, BRT, and RF-SVM ensemble models have been applied to delineate the groundwater potential zones. These models were validated with area under the receiver operating characteristics (AUROC) curve. The accuracy of RF (92%) and hybrid model (89.2%) was more efficient than BRT (85.6%) model. The results of the study will help the decision-makers, government agencies, and private sectors for sustainable planification of  groundwater resources in the study area.