Modeling regional-scale groundwater arsenic hazard in the transboundary Ganges River Delta, India and Bangladesh: Infusing physically-based model with machine learning

For the last few decades, toxic levels of arsenic (As) in groundwater from the aquifers of the Ganges River delta, India and Bangladesh, have been known to cause serious public health concerns. Innumerable studies have advocated the control of geomorphologic, geologic, hydrogeologic, biogeochemical, and anthropogenic factors on arsenic mobilization, flow, and distribution patterns within the Ganges River delta. We have developed transboundary regional-scale models for computing the probability of groundwater As concentrations to exceed the WHO permissible thresholds for drinking water of 10 μg/L within the Ganges River delta as a function of the various geomorphologic-(hydro)geologic-hydrostratigraphic-anthropogenic controlling factors, using statistical methods and artificial intelligence (AI) [i.e., machine learning] techniques namely, Random Forest (RF), Boosted Regression Trees (BRT) and Logistic Regression…