Conceptual Framework for Groundwater Vulnerability Assessment Using Physical, Experimental and Machine Learning Based Approaches in Coastal Aquifers of India
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Authors
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PANKAJ KUMAR GUPTA
Faculty of Environment, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, Canada
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BASANT YADAV
Dept. of Water Resources Development & Management, Indian Institute of Technology, Roorkee - 247 667 Uttarakhand, India
Keywords:
Machine Learning, Physical based models, Salt water intrusionAbstract
The exploitation of coastal aquifers results in intrusion of seawater and optimal management of coastal aquifers focuses mainly on adopting good operational strategies to contain aquifer salinity within a mandated limit, while simultaneously meeting the demand for water supply/recharge. Management of saltwater intrusion in coastal aquifers is thus a critical issue of modern times. In this study, we present a theoretical framework for assessing the groundwater vulnerability in coastal aquifers of India using physical, experimental, and machine learning-based approaches. The developed framework suggests the use of 2D experiment for understanding the saltwater intrusion processes and fate and transport of contaminants like fluoride and arsenic. Further, the obtained parameters from the 2D experiments will be used to develop a numerical model using a physical-based simulator (SEAWAT). Lastly, the physical-based simulator will be replaced by a machine learning-based model and later will be coupled with optimization approaches to solve the groundwater management problem in coastal aquifers. The suggested framework will be useful in developing the strategies for minimization of saltwater intrusion or maximization of freshwater pumping in coastal zones.
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