An Integrated GIS and Machine-Learning Technique for Groundwater Quality Assessment and Prediction in Southern Saudi Arabia

Abstract

One of the most critical stages for developing groundwater resources for drinking water use is assessing the water quality. The use of a Water Quality Index (WQI) is considered an effective method of evaluating water quality. The objective of this research was to evaluate the performance of six multiple artificial intelligence techniques, i.e., linear regression (stepwise), support vector regression SVM (linear and polynomial kernels), Gaussian process regression (GPR), Fit binary tree, and artificial neural network ANN (Bayesian) to predict the WQI in Jizan, Southern Saudi Arabia. A total of 145 groundwater samples were collected from shallow dug wells and boreholes tapping the phreatic aquifer. The WQI was calculated from 11 physicochemical parameters (pH, TDS, Ca2+, Mg2+, Na+, K+, Cl−, SO42−, HCO3−, NO3−, and TH). The spatial distribution results showed that higher values of Cl− and SO42− were recorded in the places close to the coastline, indicating the occurrence of seawater intrusion and salinisation. Seven wells had a WQI of greater than 300, indicating that the water was unfit for consumption. The results showed that the GPR, linear regression (stepwise), and ANN models performed best during the training and testing stages, with a high correlation of 1.00 and low errors. The stepwise fitting model indicated that pH, K+, and NO3− were the most significant variables, while HCO3− was a non-significant variable for the WQI. The GPR, stepwise regression, and ANN models performed best during the training and testing stages, with a high correlation and low errors. In contrast, the SVM and Fit binary tree models performed the worst in the training and testing phases. Based on subset regression analysis, the optimum input combination for WQI model prediction was determined as these eight input combinations with high R2 (0.975–1.00) and high Adj-R2 (0.974–1.00). The resultant WQI model significantly contributes to sustainable groundwater resource management in arid areas and generates improved prediction precision with fewer input parameters.