Application of machine learning in delineating groundwater contamination in present and climate change scenarios

Rapidly deteriorating groundwater quality due to geogenic and anthropogenic causes has impacted the lives of millions of groundwater-dependent populations globally. The situation necessitates the requirement for monitoring of groundwater resources. This brief review summarizes the use of machine learning (ML) to predict the groundwater contaminants across the world. Among the contaminants, arsenic (As) and nitrate (NO3) has been extensively modelled. Quality of data and selection of features are vital in the accurate functioning of these models. Tree based models specifically Random forest (RF) have provided more accurate prediction and are widely used than other model types. Assessment of the limitation and uncertainities of these ML models is essential in harnessing their true potential. The present study provide valuable insights that can be utilized to strategize and implement mitigation approaches to protect groundwater reserves from pollutants.