Abstract
Groundwater is a critical freshwater resource that is necessary for sustaining life. Thus, targeting prospective groundwater zones is crucial for the extraction, use, and management of water resources. In this study, we combined the remote sensing, GIS-based frequency ratio (FR), and evidential belief function (EBF) techniques into a model to delineate and quantify prospective groundwater zones. To accomplish this, we processed Shuttle Radar Topography Mission (SRTM), Landsat-8 Operational Land Imager (OLI), Sentinel-2, and rainfall data to reveal the geomorphic, hydrologic, and structural elements and climatic conditions of the study area, which is downstream of the Yellow River basin, China. We processed, quantified, and combined twelve factors (the elevation, slope, aspect, drainage density, lineament density, distance to rivers, NDVI, TWI, SPI, TRI, land use/cover, and rainfall intensity) that control the groundwater infiltration and occurrence using the GIS-based FR and EBF models to produce groundwater potential zones (GWPZs). We used the natural breaks classifier to categorize the groundwater likelihood at each location as very low, low, moderate, high, or very high. The FR model exhibited a better performance than the EBF model, as evidenced by the area under the curve (AUC) assessment of the groundwater potential predictions (FR AUCs of 0.707 and 0.734, and EBF AUCs of 0.665 and 0.690). Combining the FR and EBF models into the FR-EBF model increased the accuracy (AUC = 0.716 and 0.747), and it increased the areas of very high and moderate potentiality to 1.97% of the entire area, instead of the 0.39 and 0.78% of the FR and EBF models, respectively. The integration of remote sensing and GIS-data-driven techniques is crucial for the mapping of groundwater prospective zones.