Machine learning models for predicting water quality index: optimization and performance analysis for El Moghra, Egypt

Abstract

ABSTRACT Assessing groundwater quality is vital for irrigation, but financial constraints in developing countries often result in infrequent sampling. This study comprehensively analyzes the groundwater quality of the El Moghra aquifer in Egypt's arid Western Desert, for its suitability for irrigation uses. Detailed hydrochemical analysis and advanced machine learning (ML) techniques, including geographic information systems, were employed to enhance spatial analysis and predictive accuracy. Various ML models, such as random forest, adaptive boosting, and extreme gradient boosting (XGBoost), were optimized using Bayesian optimization to predict the irrigation water quality index (IWQI) accurately. The evaluation incorporated visual and quantitative methods, alongside ranking analysis, to validate model effectiveness. Shapley Additive exPlanations feature importance analysis and a graphical user interface (GUI) model were developed based on the best predictive model. The results indicated that the groundwater quality is generally suitable for irrigation, with XGBoost showing the best performance, achieving a root mean square error of 5.602 and a determination coefficient (R²) of 0.872. Sodium concentration was identified as the most significant factor affecting the IWQI. The GUI facilitates easy prediction of IWQI, aiding agricultural water management and resource allocation within the region.