Machine Learning Modelling of Groundwater Water Quality Index (WQI)

The dataset comprises groundwater quality data collected during a post-monsoon sampling campaign from the Jajpur district, Odisha, India. Twelve hydrochemical parameters were used as predictors for Water Quality Index (WQI) modelling. The WQI served as the response variable. The dataset was analysed using Bayesian hyperparameter–optimized artificial neural network (ANN), random forest (RF), and multiple linear regression (MLR) models. Model evaluation included cross-validation, resilience testing, predictor importance analysis This submission includes the compiled groundwater dataset, optimized model architectures, and source codes used for analysis and graphical representation.