A cooperative search algorithm-based flood forecasting framework: application across diverse Chinese catchments
Flood forecasting is crucial for disaster mitigation, particularly in regions prone to flash floods. This study introduces a novel flood forecasting framework by coupling the Geomorphological Instantaneous Unit Hydrograph (GIUH) with the Xinanjiang model and optimizing parameters using the Cooperati...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2025.1538235/full |
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| author | Jinhai Yang Lei Wen Junliang Guo Yiru Chen Yongchen Zhu Yun Wang Meihong Ma Meihong Ma |
| author_facet | Jinhai Yang Lei Wen Junliang Guo Yiru Chen Yongchen Zhu Yun Wang Meihong Ma Meihong Ma |
| author_sort | Jinhai Yang |
| collection | DOAJ |
| description | Flood forecasting is crucial for disaster mitigation, particularly in regions prone to flash floods. This study introduces a novel flood forecasting framework by coupling the Geomorphological Instantaneous Unit Hydrograph (GIUH) with the Xinanjiang model and optimizing parameters using the Cooperation Search Algorithm (CSA). Applied across six diverse Chinese catchments, the framework significantly improved computational efficiency and accuracy. Key findings demonstrate that: 1) CSA achieved high Nash-Sutcliffe Efficiency (NSE >0.9) with only 16 optimization trials on average, outperforming the SCE-UA algorithms; 2) The model performed exceptionally in data-sparse regions, achieving NSE values >0.9 even with minimal datasets; and 3) Enhanced runoff routing via GIUH enabled accurate simulation of extreme rainfall events. These results highlight the framework’s potential for operational flood forecasting and disaster management globally. Future research will expand validation datasets and explore applications across varied hydrological and climatic conditions. |
| format | Article |
| id | doaj-art-2ceb1738066e45f28e061e2dbba89028 |
| institution | Kabale University |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj-art-2ceb1738066e45f28e061e2dbba890282025-02-11T06:59:56ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-02-011310.3389/feart.2025.15382351538235A cooperative search algorithm-based flood forecasting framework: application across diverse Chinese catchmentsJinhai Yang0Lei Wen1Junliang Guo2Yiru Chen3Yongchen Zhu4Yun Wang5Meihong Ma6Meihong Ma7Shanxi Water Resources Research Institute Co., Ltd., Shanxi, ChinaGuizhou Survey/Design Research Institute for Water Resources and Hydropower, Department of Science and Technology Innovation, Guiyang, Guizhou, ChinaCollege of Hydraulic and Environment Engineering, China Three Gorges University, Yichang, ChinaFaculty of Geography, Tianjin Normal University, Tianjin, ChinaShaoxing Designstitute of Water Conservancy and Hydro-Electric Power Co., Ltd., Shanxi, ChinaFaculty of Geography, Tianjin Normal University, Tianjin, ChinaFaculty of Geography, Tianjin Normal University, Tianjin, ChinaEngineering Technology Research Center for the Prevention and Control of Mountain Torrents in the Ministry of Water Resources, Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan, ChinaFlood forecasting is crucial for disaster mitigation, particularly in regions prone to flash floods. This study introduces a novel flood forecasting framework by coupling the Geomorphological Instantaneous Unit Hydrograph (GIUH) with the Xinanjiang model and optimizing parameters using the Cooperation Search Algorithm (CSA). Applied across six diverse Chinese catchments, the framework significantly improved computational efficiency and accuracy. Key findings demonstrate that: 1) CSA achieved high Nash-Sutcliffe Efficiency (NSE >0.9) with only 16 optimization trials on average, outperforming the SCE-UA algorithms; 2) The model performed exceptionally in data-sparse regions, achieving NSE values >0.9 even with minimal datasets; and 3) Enhanced runoff routing via GIUH enabled accurate simulation of extreme rainfall events. These results highlight the framework’s potential for operational flood forecasting and disaster management globally. Future research will expand validation datasets and explore applications across varied hydrological and climatic conditions.https://www.frontiersin.org/articles/10.3389/feart.2025.1538235/fullflood forecastinggeomorphological instantaneous unit hydrograph (GIUH)cooperation search algorithmparameter optimizationdiverse catchment |
| spellingShingle | Jinhai Yang Lei Wen Junliang Guo Yiru Chen Yongchen Zhu Yun Wang Meihong Ma Meihong Ma A cooperative search algorithm-based flood forecasting framework: application across diverse Chinese catchments Frontiers in Earth Science flood forecasting geomorphological instantaneous unit hydrograph (GIUH) cooperation search algorithm parameter optimization diverse catchment |
| title | A cooperative search algorithm-based flood forecasting framework: application across diverse Chinese catchments |
| title_full | A cooperative search algorithm-based flood forecasting framework: application across diverse Chinese catchments |
| title_fullStr | A cooperative search algorithm-based flood forecasting framework: application across diverse Chinese catchments |
| title_full_unstemmed | A cooperative search algorithm-based flood forecasting framework: application across diverse Chinese catchments |
| title_short | A cooperative search algorithm-based flood forecasting framework: application across diverse Chinese catchments |
| title_sort | cooperative search algorithm based flood forecasting framework application across diverse chinese catchments |
| topic | flood forecasting geomorphological instantaneous unit hydrograph (GIUH) cooperation search algorithm parameter optimization diverse catchment |
| url | https://www.frontiersin.org/articles/10.3389/feart.2025.1538235/full |
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