Prediction of Groundwater Potential Mapping using Linear Regression and Genetic Algorithm

Groundwater capacity studies assess subsurface resources using multiple methodologies. Groundwater mapping potential estimates can help manage and schedule resources. Finding good groundwater development locations is essential for sustainable management. Geological, hydrological, and environmental data are needed to map underground water potential. Water resource management and hydrogeology can influence groundwater mapping potential. Several methods have been used to predict groundwater potential mapping. This study estimates groundwater potential using genetic algorithms and linear regression. The recommended linear regression approach estimates groundwater potential and parameters. Select the most critical groundwater potential variables and optimize the linear-regression model. Groundwater mapping potential is predicted after model optimization. An area with different hydrogeological parameters was examined to test the approach. The proposed method accurately predicts groundwater potential mapping and provides relevant water resource management data. LR-GA helps manage water assets and calculate groundwater potential. The advice is 93.7% correct. The proposed approach is ${9 0. 6 \%}$ sensitive and ${8 9. 6 3 \%}$ specific.