Groundwater Site Prediction Using Remote Sensing, GIS and Statistical Approaches: A Case Study in the Western Desert, Iraq

Abstract

Combination of remote sensing data and geographical information system (GIS) for the investigation of groundwater has become an advance approach in the researches of groundwater. The purpose of this research is to apply statistical models such as Evidential Belief Function (EBF) and Logistic Regression (LR) for mapping groundwater potential sites at Iraqi western desert (located at Al-Ramadi and Shithatha). The potential of the groundwater areas were determined depending on the spatial relationship between groundwater wells and different conditioning factors. These factors include altitude, curvature, aspect, slope, soil, normalized difference vegetation index (NDVI), topographic wetness index, fault, rainfall, stream density, stream power index, and lithology. The algorithms were used to model all layers of groundwater conditioning factors to generate groundwater probability areas. Then, the final maps included five potential classes i.e., very high, high, moderate, low and very low susceptible zones were generated. The final outcomes were validated using Area Under the Curve (AUC) algorithm. The values of success rates were 76.5% and 71.5% for EBF an LR respectively. The prediction rates for the same methods were 73.7% and 70%, respectively. The thematic maps attained from the present study indicated the capability of EBF and LR methods in groundwater potential mapping.