Articles | Volume 20, issue 1
https://doi.org/10.5194/hess-20-375-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/hess-20-375-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization
Department of Water Resources and Environment, Sun Yat-sen
University, Room 108, Building 572, Guangzhou 510275, China
Department of Water Resources and Environment, Sun Yat-sen
University, Room 108, Building 572, Guangzhou 510275, China
H. Xu
Bureau of Hydrology and Water Resources of Fujian Province.
Fuzhou, Fujian, China
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2 citations as recorded by crossref.
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Saved (preprint)
Latest update: 23 Nov 2024
Short summary
Parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological model. A method for parameter optimization with particle swam optimization (PSO) algorithm has been proposed for physically based distributed hydrological model in catchment flood forecasting and validated in southern China. It has found that the appropriate particle number and maximum evolution number of PSO algorithm are 20 and 30 respectively.
Parameter optimization is necessary to improve the flood forecasting capability of physically...
Special issue