Flood Damage Mitigation by Reservoirs through Linking Fuzzy Approach and Evolutionary Optimization
Abstract
This study proposes and evaluates a combined soft computing method to mitigate agricultural flood damage downstream of reservoirs, in which a fuzzy inference system and evolutionary optimization are linked. A flood damage function was developed by linking a Mamdani fuzzy inference system and Hydrological Engineering Centre—River Analysis System two-dimensional (HEC RAS-2D) model. First, an expert panel proposed the fuzzy rules of flood damage to develop the damage function. Then, this function was utilized in the reservoir operation model, in which evolutionary optimization methods were applied to optimize release from the reservoir. Finally, a decision-making system consisting of the two stages of evaluation were used for selecting the best algorithm. In the first stage, the performance of the penalty functions was considered to exclude some inappropriate algorithms. Then, the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) finalized the best solution of the reservoir management. Based on the results in the case study, either the firefly algorithm or the differential evolution algorithm is the best method to mitigate flood damage. The outputs corroborated the robustness of the developed method to mitigate potential flood damage. The maximum flood damage was reduced 40% compared with the natural flow. Moreover, average flood damage and inundation duration in the simulated period were mitigated considerably. It is recommended to apply the proposed method in flood mitigation studies in which overcoming flood damage data scarcity is a challenge.
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Data Availability Statement
All data and materials that support the findings of this study are available from the corresponding author upon reasonable request.
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Natural Hazards Review
Volume 24 • Issue 2 • May 2023
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© 2023 American Society of Civil Engineers.
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Received: Jul 2, 2022
Accepted: Oct 31, 2022
Published online: Jan 5, 2023
Published in print: May 1, 2023
Discussion open until: Jun 5, 2023
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