https://scholars.lib.ntu.edu.tw/handle/123456789/631063
標題: | Exploring a multi-objective cluster-decomposition framework for optimizing flood control operation rules of cascade reservoirs in a river basin | 作者: | Zhu, Di Chen, Hua Zhou, Yanlai Xu, Xinfa Guo, Shenglian FI-JOHN CHANG Xu, Chong Yu |
關鍵字: | Cascade reservoirs; Cluster-decomposition; Flood control operation; K-means method; Multi-objective optimization; NSGA-II | 公開日期: | 1-十一月-2022 | 出版社: | ELSEVIER | 卷: | 614 | 來源出版物: | Journal of Hydrology | 摘要: | Multi-objective flood control operation of cascade reservoirs is a vital issue in river basin management. However, traditional multi-objective approaches commonly provide one operation scheme only and fail to offer decision-makers with more Pareto-front options. This study explores a multi-objective cluster-decomposition framework for optimizing the flood control operation rules of cascade reservoirs in a river basin. The proposed framework involves a multi-objective optimization module, a cluster-decomposition module, and an evaluation and sorting module. The multi-objective cluster-decomposition framework simultaneously deals with three objectives: to minimize the flood peaks of flood control points (O1); to minimize the reservoir capacity used for flood control (O2); and to minimize the flood diversion volume of the flood detention area (O3). The complex flood control system composed of two cascade reservoirs, four navigation-power junctions, one flood detection area, and three flood control points in the Ganjiang River basin of China constitutes the case study. The results demonstrate that the proposed framework can significantly improve the comprehensive benefits of the cascade reservoirs, where the maximum reduction in objectives O1–O3 is 2071 m3/s (the improvement rate is 2.64 %), 219 million m3 (the improvement rate is 44.60 %), and 167 million m3 (the improvement rate is 78.13 %), respectively. Furthermore, in contrast to the traditional multi-attribute evaluation method, the proposed framework can effectively identify compromised decisions through a cluster-decomposition module, which provides beneficial trade-off guidance in making a sound decision upon Pareto-front options. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143777145&doi=10.1016%2fj.jhydrol.2022.128602&partnerID=40&md5=60f822e70cad3e6fc8e540d22d3a96e0 https://scholars.lib.ntu.edu.tw/handle/123456789/631063 |
ISSN: | 00221694 | DOI: | 10.1016/j.jhydrol.2022.128602 |
顯示於: | 生物環境系統工程學系 |
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