ORIGINAL RESEARCH article
Front. Water
Sec. Water Resource Management
Volume 7 - 2025 | doi: 10.3389/frwa.2025.1606096
This article is part of the Research TopicWater, River and Human: Geographical and Environmental Concerns and Dilemma in the AnthropoceneView all articles
Stochastic Multi-Objective Optimization for Flood Control in Multi-Reservoir Systems: An Adaptive Progressive Hedging Approach with Scenario Clustering
Provisionally accepted
- 1Department of Quantitative Methods, University of Sousse, Sousse, Tunisia
- 2Department of Quantitative Methods, College of Business, King Faisal University, Hofuf, Saudi Arabia
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Flood-prone regions face growing challenges due to climate-induced variability, rapid urbanization, and competing demands on water infrastructure. This study presents a robust and scalable framework for coordinating flood-season reservoir operations under uncertainty in large, interconnected water systems. Focusing on Tunisia's Medjerda River basin, we develop a stochastic multi-objective optimization model that balances downstream flood risk reduction with long-term water storage security. The approach integrates ensemble-based inflow scenarios, high-resolution hydraulic simulations, and an adaptive version of the Progressive Hedging Algorithm, enhanced by K-means scenario clustering for computational efficiency. Results show that the proposed method yields operationally feasible release policies that perform consistently across diverse flood conditions, while significantly reducing computational costs. By combining hydrological realism with optimization scalability, this work supports the design of resilient and anticipatory flood management strategies in semi-arid regions, directly contributing to global efforts toward sustainable water governance (SDG 6), climate resilience (SDG 13), and disaster risk reduction in human settlements (SDG 11).
Keywords: Stochastic multi-objective optimization, Flood control, Multi-reservoir systems, Progressive hedging, Scenario clustering, Climate-resilient water management
Received: 04 Apr 2025; Accepted: 14 Aug 2025.
Copyright: © 2025 Argoubi and MILI. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Khaled MILI, Department of Quantitative Methods, College of Business, King Faisal University, Hofuf, Saudi Arabia
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