Total alkalinity (TAC) plays a pivotal role in buffering acid–base fluctuations and maintaining pH stability in aquatic ecosystems. This study presents a data-driven approach to model TAC using decision tree regression, applied to a comprehensive dataset of 454 water samples collected in diverse aquatic environments of the Médéa region, Algeria. Twenty physicochemical parameters, including concentrations of bicarbonates, hardness, major ions, and trace elements, were analyzed as input features. The decision tree algorithm was optimized using the Dragonfly metaheuristic algorithm coupled with 5-fold cross-validation. The optimized model (DTDA) demonstrated exceptional predictive performance, with a correlation coefficient R of 0. 9999, and low prediction errors (RMSE = 0. 3957, MAE = 0. 3572, and MAPE = 0. 4531). External validation on an independent dataset of 68 samples confirmed the model’s robustness (R = 0. 9999; RMSE = 0. 4223; MAE = 0. 3871, and MAPE = 0. 4931). The tree structure revealed that total hardness (threshold: 78. 5 °F) and bicarbonate concentration (threshold: 421. 68 mg/L) were the most influential variables in TAC determination. The model offers not only accurate predictions but also interpretable decision rules, allowing the identification of critical physicochemical thresholds that govern alkalinity. These findings provide a valuable tool for anticipating pH instability and guiding water quality management and protection strategies in freshwater ecosystems.
Tahraoui et al. (2025) studied this question.