Analysis of Groundwater Level Fluctuations using AI & ML - A Case Study on Arkavathi Watershed, Karnataka, India
Keywords:
Arkavathi Watershed, Groundwater, Machine Learning, Mann-Kendall Method, Random ForestAbstract
The goal of this study is to forecast and assess groundwater levels in the Arkavathi Watershed by using cutting-edge Artificial Intelligent and Machine Learning approaches. Groundwater level and recharge is influenced by many factors like land use, soil properties, and climate, and recharge is essential for the sustainable management of water resources. To predict groundwater levels, statistical methods like Mann-Kendall (M-K) test and Sen's Slope Estimator and supervised learning systems like Random Forest, Gradient Boosting Machines, and Neural Networks use meteorological inputs and historical data. In the present work, the dataset acquired for the duration 2014 to 2023 from the Central Ground Water Board, Bangalore (CGWB) offers important information on interannual and seasonal trends in Arkavathi watershed. Out of 37 wells 76% of wells exhibited dropping trends, while 24% indicated growing trends. The significant declines in groundwater levels are seen in the northern and southwestern areas, with losses reaching up to 15 meters during the monsoon seasons probably because of inadequate infiltration, surface runoff, and over-extraction. With mean water levels varying between 10 and 15 meters below ground level, the center basin exhibits only slight changes, suggesting a more balanced recharge-extraction relationship. In the northwest and southeast, alluvial deposits and water-saving infrastructure enable the regular maintenance of stable groundwater levels below 12 meters. Further, the use of AI & ML techniques indicates that variations in rainfall patterns affects the groundwater levels. Also the use of ML algorithms helps to identify the most effective locations for artificial recharge structures, suggesting groundwater management decision-making can be made easier when artificial intelligence and machine learning are used to improve the accuracy of groundwater levels and recharge estimations.
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