Open Access

ARTICLE

An IoT-Enabled Hybrid DRL-XAI Framework for Transparent Urban Water Management

Qamar H. Naith^1,*, H. Mancy^2,3

1 Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21959, Saudi Arabia
2 Department of Computer Science, College of Engineering and Computer Sciences, Prince Sattam Bin Abdulaziz University, Al-kharj, 11942, Saudi Arabia
3 Department of Mathematics, Faculty of Science (Girls), Al-Azhar University, Cairo, 11765, Egypt

* Corresponding Author: Qamar H. Naith. Email:

Computer Modeling in Engineering & Sciences 2025, 144(1), 387-405. https://doi.org/10.32604/cmes.2025.066917

Received 21 April 2025; Accepted 16 June 2025; Issue published 31 July 2025

Abstract

Effective water distribution and transparency are threatened with being outrightly undermined unless the good name of urban infrastructure is maintained. With improved control systems in place to check leakage, variability of pressure, and conscientiousness of energy, issues that previously went unnoticed are now becoming recognized. This paper presents a grandiose hybrid framework that combines Multi-Agent Deep Reinforcement Learning (MADRL) with Shapley Additive Explanations (SHAP)-based Explainable AI (XAI) for adaptive and interpretable water resource management. In the methodology, the agents perform decentralized learning of the control policies for the pumps and valves based on the real-time network states, while also providing human-understandable explanations of the agents’ decisions, using SHAP. This framework has been validated on five very diverse datasets, three of which are real-world scenarios involving actual water consumption from NYC and Alicante, with the other two being simulation-based standards such as LeakDB and the Water Distribution System Anomaly (WDSA) network. Empirical results demonstrate that the MADRL + SHAP hybrid system reduces water loss by up to 32%, improves energy efficiency by up to 25%, and maintains pressure stability between 91% and 93%, thereby outperforming the traditional rule-based control, single-agent DRL (Deep Reinforcement Learning), and XGBoost + SHAP baselines. Furthermore, SHAP-based interpretation brings transparency to the proposed model, with the average explanation consistency for all prediction models reaching 88%, thus further reinforcing the trustworthiness of the system on which the decision-making is based and empowering the utility operators to derive actionable insights from the model. The proposed framework addresses the critical challenges of smart water distribution.

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Keywords

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