Efficient Frequency ratio-Graphical adaptive tree-weighted support vector machine based ground water potential mapping utilizing remote sensing and GIS

Groundwater (GW) sources are a valuable environmental asset, because they may be used for household, agricultural, and industrial reasons. The native population requires sufficient groundwater management since GW is now a significant source of farm and potable water. Groundwater needs have significantly increased as a result of populace growth, efficient farming techniques, and commercial uses. Hence, GW potential mapping is the sustainable development goals of the significant water source. The graphical adaptive tree-weighted support vector machine (GATSVM) approach of the ML algorithm is then implemented after determining the frequency ratio (FRR) for SC and TR. To create and analyze groundwater potential mapping, a hybrid method called FRR-GATSVM is proposed. FRR-GATSVM measured accuracy, sensitivity, specificity, precision, recall, and root mean square error (RMSE) and showed results for each of these performance indicators. Various existing methods were also compared with indicators to find the proposed method’s effectiveness. In places with a shortage of data, the ML approaches used in this work demonstrated successful modeling of groundwater potential. By recognizing regions with significant groundwater potential, the study’s findings could be applied to enhance groundwater resources sustainably.