Identification of Groundwater Potential Zones Using Machine Learning Algorithms and Geospatial Techniques

In contemporary times, groundwater has emerged as the predominant source of irrigation in India, alongside serving as the principal provider of potable water in several regions. The rise in population has led to a surge in water consumption, thus leading to a decline in groundwater levels. Groundwater plays a pivotal role as a vital source that contributes significantly to the annual water supply. This research investigates various approaches to identify potential groundwater zones by employing geographic information systems (GIS) and remote sensing, while also including machine learning techniques. These methodologies provide a reliable means to map areas with high groundwater potential. The mapping of groundwater zones encompasses a wide spectrum of procedural variations. To effectively manage groundwater zones, it is imperative to consider multiple criteria, encompassing soil characteristics, drainage density, land utilization, precipitation patterns, land cover types, slope gradients, geological composition, geomorphological features, and contour variations. This paper examines the approaches employed in groundwater mapping, with a particular focus on the utilization of satellite remote sensing and other relevant data sources in the development of these methodologies. These tactics employ a combination of traditional and innovative approaches. Thematic layers are employed for the objectives of analyzing, mapping, and identifying groundwater potential. The evaluation of the significance and magnitude of each thematic layer is conducted in connection to the geographical location, zones of possible groundwater availability, and prevailing groundwater conditions. Maps of groundwater potential zones were generated by the use of machine learning algorithms. These maps were then classified into five distinct categories, which are as follows: very low, low, moderate, high, and very high.