Comparative Assessment of Artificial Intelligence Models for Predicting Water Quality in a Reverse Osmosis Desalination Plant

= 0.999; RMSE = 4.90), making it suitable for daily operation. The analysis further indicates that the performance of complex models, such as the MLP, is strongly influenced by the limited sample size (MAPE = 65.45%), illustrating the sensitivity of deep learning approaches in small-data contexts. While this frames the applicability of the results within the scope of the available dataset, it remains representative of the operational conditions commonly encountered in desalination plants with restricted yet meaningful datasets. Overall, XGBoost, RF, SVR, and LR demonstrate significant potential for predictive monitoring and sustainable optimization of desalination processes.