Comparative analysis of GIS and RS based models for delineation of groundwater potential zone mapping

Abstract

Groundwater is a crucial natural resource that varies in quality and quantity across Khyber Pakhtunkhwa (KPK), Pakistan. Increased population and urbanization place enormous demands on groundwater supplies, reducing both their quality and quantity. This research aimed to delineate the groundwater potential zone in the Kohat region, Pakistan by integrating twelve thematic layers. In the current research, Groundwater Potential Zone (GWPZ) were created by implementing Weight of Evidence (WOE), Frequency Ratio (FR), and Information Value (IV) models of the Kohat region. In this study, we used Sentinel-2 satellite data were utilized to generate an inventory map of groundwater using machine learning algorithms in Google Earth Engine (GEE). Furthermore, the validation was done with a field survey and ground data. The inventory data was divided into training (80%) and testing (20%) datasets. The WOE, FR, and IV models are applied to assess the relationship between inventory data and groundwater factors to generate the GWPZ of the Kohat region. Finally, the current research results of Area Under Curve (AUC) technique for WOE, FR, and IV models were 88%, 91%, and 89%. The final GWPZ can aid in better future planning for groundwater exploration, management, and supply of water in the Kohat region.