Advanced machine vision techniques for groundwater level prediction modeling geospatial and statistical research

This study utilizes three models, namely Weight of Evidence (WOE), Frequency Ratio (FR), and Index of Vulnerability (IV), to identify groundwater potential zones (GWPZ) in southern Khyber Pakhtunkhwa, Nowshera District, Pakistan. We incorporated a total of twelve variables, encompassing elevation, slope, distance to the rivers, rainfall, curvature, drainage density, land use/land cover, topographic wetness index, height above the nearest drainage, NDVI, distance to the roads, and soil type, utilizing ArcGIS 10.8. The AUROC (Area Under the Receiver Operating Characteristic) assessed the dependability of the models. GWPZ was categorized into five classifications: very low, low, moderate, high, and very high. The WOE model produced distributions of 10.14 % (262.09 km2), 19.58 % (506.00 km2), 26.75 % (691.10 km2), 27.10 % (700.18 km2), and 16.40 % (423.75 km2) respectively. The FR yielded 20.93 % (538.90 km2), 32.38 % (833.53 km2), 18.92 % (487.14 km2), 13.13 % (337.94 km2), and 14.61 % (376.07 km2). The IV model resulted in 14.41 % (372.46 km2), 17.17 % (443.67 km2), 29.01 % (749.52 km2), 25.85 % (667.97 km2), and 13.53 % (349.50 km2). The AUC-ROC for WOE, FR, and IV were 58.06%, 87.53%, and 84.98%, respectively. All models accurately defined the GWPZ, with the FR approach demonstrating notable potential. These findings provide vital information for managing groundwater resources and the design of metropolitan areas. Our developed methodology can be used in places with comparable characteristics. It is a valuable tool for policy- makers interested in sustainable groundwater management. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.