A Hydrodynamic Model and Data-Driven Evolutionary Multi-Objective Optimization Algorithm Based Optimal Operation Method for Multi-barrage Flood Control-Reference-Cited by-同舟云学术

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A Hydrodynamic Model and Data-Driven Evolutionary Multi-Objective Optimization Algorithm Based Optimal Operation Method for Multi-barrage Flood Control

Published:2024-04-23 Issue:11 Volume:38 Page:4323-4341
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Li Xuan,Zhou Xiaoping,Hou Jingming,Liu Yuan,Xue Shuhong,Ma Huan,Su Bowen

Funder

Key science and technology projects of Power China

Major science and technology projects of Power China Northwest Engineering Corporation Limited

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11269-024-03867-z.pdf

Reference33 articles.

1. Chen J, Zhong PA, Xu B, Zhao YF (2015) Risk analysis for real-time Flood Control Operation of a Reservoir. J Water Resour Plan Manag 141(8). https://doi.org/10.1061/(asce)wr.1943-5452.0000490

2. Chen J, Zhong PA, Liu WF, Wan XY, Yeh WWG (2020) A multi-objective risk management model for real-time flood control optimal operation of a parallel reservoir system. J Hydrol 590. https://doi.org/10.1016/j.jhydrol.2020.125264

3. Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279

4. Ham YG (2023) Deep learning shows how global warming affects daily rainfall. Nature. https://doi.org/10.1038/d41586-023-02803-2

5. Hou JM, Liang QH, Simons F, Hinkelmann R (2013) A stable 2D unstructured shallow flow model for simulations of wetting and drying over rough terrains. Comput Fluids 82:132–147. https://doi.org/10.1016/j.compfluid.2013.04.015

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