Approaches to Mapping and Zonation of Groundwater Potential Mapping through Multi-Agent Optimization in GIS

The groundwater resources utilization has encountered significant difficulties as a result of the depletion of surface-water supplies and recurrent droughts. Moreover, groundwater potential status detailed analyses have been necessary for the best handling of such valuable resources. In order to determine the water shortage and analyse the water index for Thiruvallur district, Tamil Nadu, a method integrating Remote Sensing (RS), Geographic Information Systems (GIS), and Deep Learning (DL) algorithm (i.e., Convolutional Neural Network (CNN)) was utilised in this work and multistage optimization is used for get accurate result. Initially groundwater control elements, comprising nine topographic variables, two hydrological variables, two geological variables, the kind of forest, the type of soil, and the type of land use, have been mapped using RS and GIS information for this reason. Second, predicated on the provided method and the training information, the groundwater potential has been evaluated and plotted. This study categorizes the groundwater and classify them as either very high, high, moderate, low, or very low. In conclusion, the proposed CNN model is capable of assessing the water index and detecting instances of water scarcity. The presented model was evaluated using the following metrics: accuracy, precision, precision, ROC, MAE, MCC, and classification error rate (CEP). The effectiveness of the described method was evaluated by comparing it to current methodologies. The proposed methods have ROC values of 98.76, an accuracy of 99.01%, a precision of 98.87%, and a f1-score of 98.16%.