Application of Harris Hawks Optimization Algorithm in Optimization of Generalized Nonlinear Muskingum Parameters ——A Case Study of the Luohe River
The Muskingum model plays an important role in river flood simulation,and its simulation accuracy relies on the optimal selection of parameters.To address the current challenges in parameter calibration for the Muskingum model,such as complex solution processes and low accuracy,the use of the Harris...
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Editorial Office of Pearl River
2024-01-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.02.008 |
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| author | CHEN Haitao ZHAO Zhijie |
| author_facet | CHEN Haitao ZHAO Zhijie |
| author_sort | CHEN Haitao |
| collection | DOAJ |
| description | The Muskingum model plays an important role in river flood simulation,and its simulation accuracy relies on the optimal selection of parameters.To address the current challenges in parameter calibration for the Muskingum model,such as complex solution processes and low accuracy,the use of the Harris Hawks optimization (HHO) algorithm was proposed to optimize its parameters.HHO algorithm has a wide range of global search capabilities,with fewer parameters to be adjusted.Taking Luohe River,a tributary of the Yellow River,as the research object,the generalized nonlinear Muskingum model was used to simulate the flood in the Yiyang-Baimasi section of the river.The parameters were optimized by employing the HHO algorithm,particle swarm optimization (PSO) algorithm,and ant colony optimization (ACO) algorithm,respectively.The results show that the generalized nonlinear Muskingum model based on the HHO algorithm achieved high simulation accuracy in the Yiyang-Baimasi section of the Luohe River,with a Min.SSD of 1 237 and the flood peak error (DPO) of only 5,outperforming those obtained through optimization using PSO algorithm and ACO algorithm.The results are suitable for application in flood forecasting in the Yiyang-Baimasi section of the Luohe River. |
| format | Article |
| id | doaj-art-6e09a1123c4c45068f512a49566bd1eb |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-6e09a1123c4c45068f512a49566bd1eb2025-01-15T03:00:21ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352024-01-014550118404Application of Harris Hawks Optimization Algorithm in Optimization of Generalized Nonlinear Muskingum Parameters ——A Case Study of the Luohe RiverCHEN HaitaoZHAO ZhijieThe Muskingum model plays an important role in river flood simulation,and its simulation accuracy relies on the optimal selection of parameters.To address the current challenges in parameter calibration for the Muskingum model,such as complex solution processes and low accuracy,the use of the Harris Hawks optimization (HHO) algorithm was proposed to optimize its parameters.HHO algorithm has a wide range of global search capabilities,with fewer parameters to be adjusted.Taking Luohe River,a tributary of the Yellow River,as the research object,the generalized nonlinear Muskingum model was used to simulate the flood in the Yiyang-Baimasi section of the river.The parameters were optimized by employing the HHO algorithm,particle swarm optimization (PSO) algorithm,and ant colony optimization (ACO) algorithm,respectively.The results show that the generalized nonlinear Muskingum model based on the HHO algorithm achieved high simulation accuracy in the Yiyang-Baimasi section of the Luohe River,with a Min.SSD of 1 237 and the flood peak error (DPO) of only 5,outperforming those obtained through optimization using PSO algorithm and ACO algorithm.The results are suitable for application in flood forecasting in the Yiyang-Baimasi section of the Luohe River.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.02.008flood forecastinggeneralized nonlinear Muskingum modelHarris Hawks optimization (HHO) algorithmparameter calibration |
| spellingShingle | CHEN Haitao ZHAO Zhijie Application of Harris Hawks Optimization Algorithm in Optimization of Generalized Nonlinear Muskingum Parameters ——A Case Study of the Luohe River Renmin Zhujiang flood forecasting generalized nonlinear Muskingum model Harris Hawks optimization (HHO) algorithm parameter calibration |
| title | Application of Harris Hawks Optimization Algorithm in Optimization of Generalized Nonlinear Muskingum Parameters ——A Case Study of the Luohe River |
| title_full | Application of Harris Hawks Optimization Algorithm in Optimization of Generalized Nonlinear Muskingum Parameters ——A Case Study of the Luohe River |
| title_fullStr | Application of Harris Hawks Optimization Algorithm in Optimization of Generalized Nonlinear Muskingum Parameters ——A Case Study of the Luohe River |
| title_full_unstemmed | Application of Harris Hawks Optimization Algorithm in Optimization of Generalized Nonlinear Muskingum Parameters ——A Case Study of the Luohe River |
| title_short | Application of Harris Hawks Optimization Algorithm in Optimization of Generalized Nonlinear Muskingum Parameters ——A Case Study of the Luohe River |
| title_sort | application of harris hawks optimization algorithm in optimization of generalized nonlinear muskingum parameters a case study of the luohe river |
| topic | flood forecasting generalized nonlinear Muskingum model Harris Hawks optimization (HHO) algorithm parameter calibration |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.02.008 |
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