Artificial Intelligence for Predicting and Optimizing Water Quality in Aquatic Systems
Abstract
Maintaining optimal water quality is essential for the sustainability of large aquarium systems, yet conventional treatment technologies such as reverse osmosis and membrane filtration remain energy intensive and expensive. Although artificial intelligence has been widely applied for water quality prediction, most existing studies focus mainly on predictive modeling and rarely integrate optimization, inverse prediction, interpretability, and comparative evaluation within a unified decision support framework. This study develops an interpretable artificial intelligence framework for proactive water quality management by predicting, optimizing, and inversely estimating two key indicators: electrical conductivity (EC) and total dissolved solids (TDS). A database of 110 raw water samples containing 21 routinely measured physicochemical parameters was used to develop decision tree ensemble models. RUSBoost and AdaBoostM5 models were optimized using a genetic algorithm and evaluated against optimized baseline models, including Regression Tree, Support Vector Regression, and Gaussian Process Regression, under the same validation conditions. The proposed boosting models achieved superior predictive performance compared with the baseline approaches. The RUSBoost model provided the best performance for conductivity prediction, achieving an overall correlation coefficient of R = 0.9996 (R² = 0.9993), whereas AdaBoostM5 achieved the highest accuracy for TDS prediction with R = 0.9995 (R² = 0.9990). Both models exhibited very low prediction errors, with overall RMSE values of 2.99 μS cm⁻¹ for conductivity and 2.62 mg L⁻¹ for TDS. Cross-validation and external validation using an independent water source confirmed the robustness and stability of the developed framework. Predictor importance analysis, representative decision tree structures, and constrained optimization enabled the identification of dominant physicochemical factors and feasible water quality configurations. The developed MATLAB based decision support application integrates prediction, optimization, and inverse prediction functionalities. The proposed framework provides an interpretable and practical tool for proactive aquatic water quality management, supporting more efficient control of physicochemical parameters and reducing unnecessary dependence on costly treatment processes.
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BibTeX
@article{bouallouche2026artificial,
title = {{Artificial Intelligence for Predicting and Optimizing Water Quality in Aquatic Systems}},
author = {Rachida Bouallouche and Nour El Houda Daoudi and Abdeldjebar Daoudi and Hichem Tahraoui and Kamilia Madi and Reguia Boudraa and Zulzamri Salleh and Ahmad Zawawi Jamaluddin and Nassim Moula and Noureddine Nasrallah and Jie Zhang and Abdeltif Amrane},
journal = {Journal of Hazardous Materials Advances},
year = {2026},
month = jul,
doi = {10.1016/j.hazadv.2026.101459},
url = {https://hal.science/hal-05741124v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial Intelligence for Predicting and Optimizing Water Quality in Aquatic Systems
AU - Rachida Bouallouche
AU - Nour El Houda Daoudi
AU - Abdeldjebar Daoudi
AU - Hichem Tahraoui
AU - Kamilia Madi
AU - Reguia Boudraa
AU - Zulzamri Salleh
AU - Ahmad Zawawi Jamaluddin
AU - Nassim Moula
AU - Noureddine Nasrallah
AU - Jie Zhang
AU - Abdeltif Amrane
PY - 2026
DA - 2026/07/31
JO - Journal of Hazardous Materials Advances
DO - 10.1016/j.hazadv.2026.101459
UR - https://hal.science/hal-05741124v1
AB - Maintaining optimal water quality is essential for the sustainability of large aquarium systems, yet conventional treatment technologies such as reverse osmosis and membrane filtration remain energy intensive and expensive. Although artificial intelligence has been widely applied for water quality prediction, most existing studies focus mainly on predictive modeling and rarely integrate optimization, inverse prediction, interpretability, and comparative evaluation within a unified decision support framework. This study develops an interpretable artificial intelligence framework for proactive water quality management by predicting, optimizing, and inversely estimating two key indicators: electrical conductivity (EC) and total dissolved solids (TDS). A database of 110 raw water samples containing 21 routinely measured physicochemical parameters was used to develop decision tree ensemble models. RUSBoost and AdaBoostM5 models were optimized using a genetic algorithm and evaluated against optimized baseline models, including Regression Tree, Support Vector Regression, and Gaussian Process Regression, under the same validation conditions. The proposed boosting models achieved superior predictive performance compared with the baseline approaches. The RUSBoost model provided the best performance for conductivity prediction, achieving an overall correlation coefficient of R = 0.9996 (R² = 0.9993), whereas AdaBoostM5 achieved the highest accuracy for TDS prediction with R = 0.9995 (R² = 0.9990). Both models exhibited very low prediction errors, with overall RMSE values of 2.99 μS cm⁻¹ for conductivity and 2.62 mg L⁻¹ for TDS. Cross-validation and external validation using an independent water source confirmed the robustness and stability of the developed framework. Predictor importance analysis, representative decision tree structures, and constrained optimization enabled the identification of dominant physicochemical factors and feasible water quality configurations. The developed MATLAB based decision support application integrates prediction, optimization, and inverse prediction functionalities. The proposed framework provides an interpretable and practical tool for proactive aquatic water quality management, supporting more efficient control of physicochemical parameters and reducing unnecessary dependence on costly treatment processes.
SN - 2772-4166
ER -