An Integrated EWQI-Guided Machine Learning Framework for Groundwater Quality: Regression and Multi-Class Classification under Iraqi Standards

Groundwater is a major source of water supply in Diyala Governorate, Iraq. This study proposes an integrated framework for groundwater quality assessment and forecasting by combining the Entropy Weighted Water Quality Index (EWQI) with machine learning algorithms. A total of 855 groundwater samples were collected, of which 853 were retained after preprocessing. The EWQI was calculated using ten hydrochemical parameters for each sample, namely pH, total dissolved solids (TDS), K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, and NO3−. Based on the Iraqi Standard Drinking Water Limits (IQS), EWQI values were categorized into five water quality classes: Excellent, Good, Medium, Poor, and Extremely Poor. For predictive analysis, both regression and classification models were developed using support vector machine (SVM), random forest (RF), backpropagation multilayer perceptron (BP-MLP), and one-dimensional convolutional neural network (1D-CNN) algorithms under three feature scenarios: (1) hydrochemical variables only, (2) hydrochemical variables combined with well properties, and (3) hydrochemical variables, well properties, and spatial coordinates. Model hyperparameters were optimized using grid search with 10-fold cross-validation on the training set, and model performance was evaluated using an 80/20 stratified split. Among the tested models, SVM demonstrated the best performance for EWQI prediction in the first scenario (hydrochemical variables only), achieving RMSE = 4.14, MAE = 0.93, and R2 = 0.999. It also produced the highest classification performance, with an accuracy of 0.971 and a macro-F1 score of 0.973. Meanwhile, RF showed consistently strong and balanced classification performance across all scenarios, reaching a macro-F1 score of 0.961. These findings highlight the effectiveness of integrating EWQI with machine learning techniques for reliable groundwater quality assessment and prediction, thereby supporting sustainable water resource management in Diyala Governorate.