Groundwater Depletion and Sustainability : A Methodology Utilizing Artificial Intelligence and Earth Observation Systems: Volume Two

This contributed volume, the second in a set of two, details how Artificial Intelligence (AI) and Earth observation systems can effectively improve the prediction of groundwater quality and support decision-making in arid and semi-arid regions.

This contributed volume, the second in a set of two, details how Artificial Intelligence (AI) and Earth observation systems can effectively improve the prediction of groundwater quality and support decision-making in arid and semi-arid regions. Earth observation systems, including remote sensing and geographic information systems (GIS), play a crucial role in assessing and monitoring groundwater quality. Remote sensing data, such as satellite imagery, can provide valuable information on land cover, vegetation indices, and water quality parameters. GIS tools enable the spatial analysis and visualization of groundwater quality data. AI and Earth observation-based methods support effective water resource management by identifying suitable areas for artificial groundwater recharge (AGR) and assessing the impact of pollution on water resources. These techniques help formulate conservation policies and sustainable water management strategies. Various AI techniques, including ANN, SVM, KNN, and decision trees, have been applied to model groundwater quality and predict water quality indices. These models capture complex relationships between hydro chemical parameters and groundwater quality, enabling accurate predictions and informed decision-making. The application of AI and Earth observation systems in groundwater quality prediction contributes to the sustainability of water resources. Identifying pollution sources, assessing water quality, and guiding decision-making processes support preserving and managing water resources in arid and semi-arid regions.