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Comparison of flood simulation capabilities of a hydrologic model and a machine learning model

文献类型：期刊论文

作者Liu, Lu1,2; Liu, Xiaomang1; Bai, Peng1; Liang, Kang1; Liu, Changming1
刊名INTERNATIONAL JOURNAL OF CLIMATOLOGY
出版日期2022-06-07
页码11
关键词flood simulation LSTM model model comparison SIMHYD model
ISSN号0899-8418
DOI10.1002/joc.7738
通讯作者Liu, Xiaomang(liuxm@igsnrr.ac.cn)
英文摘要Machine learning models have been widely used for flood simulation. Few studies have compared the flood simulation capabilities of machine learning models and hydrologic models. This study compared the flood simulation capabilities of the SIMHYD hydrologic model and the LSTM machine learning model in 232 basins with different climate conditions. The results show that although the LSTM model significantly outperforms the SIMHYD model in the calibration period, it has significant performance degradation in the validation period. Basin characteristics had limited impacts on the performance difference between the LSTM model and the SIMHYD model. The extension of the calibration period improves the performance of the LSTM model, while it has a limited impact on the performance of the SIMHYD model. Thus, machine learning models are recommended for simulating floods if enough training data are available, otherwise, hydrologic models could be a better choice. This study is helpful to the choice of flood simulation models in different situations.
WOS关键词GENETIC ALGORITHM ; NEURAL-NETWORKS ; RUNOFF ; PERFORMANCE ; CLIMATE ; EVAPOTRANSPIRATION ; CATCHMENTS ; STREAMFLOW ; RISK
资助项目National Natural Science Foundation of China[41922050] ; Youth Innovation Promotion Association, Chinese Academy of Sciences[2018067]
WOS研究方向Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:000806991800001
出版者WILEY
资助机构National Natural Science Foundation of China ; Youth Innovation Promotion Association, Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/179182]  
专题中国科学院地理科学与资源研究所
通讯作者Liu, Xiaomang
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Lu,Liu, Xiaomang,Bai, Peng,et al. Comparison of flood simulation capabilities of a hydrologic model and a machine learning model[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2022:11.
APA Liu, Lu,Liu, Xiaomang,Bai, Peng,Liang, Kang,&Liu, Changming.(2022).Comparison of flood simulation capabilities of a hydrologic model and a machine learning model.INTERNATIONAL JOURNAL OF CLIMATOLOGY,11.
MLA Liu, Lu,et al."Comparison of flood simulation capabilities of a hydrologic model and a machine learning model".INTERNATIONAL JOURNAL OF CLIMATOLOGY (2022):11.

入库方式： OAI收割

来源：地理科学与资源研究所

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