Flow simulation based on MISO and LSTM models in the Yangtze River-Dongting Lake System
Study region: The study region is the Yangtze River-Dongting Lake system, the largest regulated river-lake system in China. Study focus: Flow simulation of the Yangtze River–Dongting Lake system has long been recognized as a critical and challenging issue in the Yangtze River basin. Current approach...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Journal of Hydrology: Regional Studies |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581825002940 |
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| _version_ | 1850060393692528640 |
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| author | Chenglong Li Shenglian Guo Zhen Cui Xin Xiang |
| author_facet | Chenglong Li Shenglian Guo Zhen Cui Xin Xiang |
| author_sort | Chenglong Li |
| collection | DOAJ |
| description | Study region: The study region is the Yangtze River-Dongting Lake system, the largest regulated river-lake system in China. Study focus: Flow simulation of the Yangtze River–Dongting Lake system has long been recognized as a critical and challenging issue in the Yangtze River basin. Current approaches rely on traditional hydrological and hydrodynamic methods, but they face limitations such as difficulty obtaining required data set, complex modeling processes, long-time consumption, and insufficient accuracy. This study proposed the multi-input and single-output (MISO) model based on system dynamics principles and the long short-term memory (LSTM) neural network model based on deep learning for flow simulation in the Yangtze River-Dongting Lake system. New hydrological insights for the region: The proposed MISO and LSTM models can achieve accurate flow simulation results using only recorded flow data and rainfall data in the Yangtze River-Dongting Lake system. Both models demonstrate significantly higher computational efficiency than traditional hydrodynamic models, the MISO model can complete simulation within one second, while the LSTM model has the higher Nash-Sutcliffe efficiency coefficients and the smaller relative errors during the training and validation periods, the peak discharge relative errors (PRE) is ranging from −1.40–1.81 % across twelve flood events. This study has provided a novel approach for flow simulation in the complex river-lake system. |
| format | Article |
| id | doaj-art-ea4e503482564ec5a9ebd14370ece1b8 |
| institution | DOAJ |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-ea4e503482564ec5a9ebd14370ece1b82025-08-20T02:50:33ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-08-016010246910.1016/j.ejrh.2025.102469Flow simulation based on MISO and LSTM models in the Yangtze River-Dongting Lake SystemChenglong Li0Shenglian Guo1Zhen Cui2Xin Xiang3State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, ChinaCorresponding author.; State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, ChinaStudy region: The study region is the Yangtze River-Dongting Lake system, the largest regulated river-lake system in China. Study focus: Flow simulation of the Yangtze River–Dongting Lake system has long been recognized as a critical and challenging issue in the Yangtze River basin. Current approaches rely on traditional hydrological and hydrodynamic methods, but they face limitations such as difficulty obtaining required data set, complex modeling processes, long-time consumption, and insufficient accuracy. This study proposed the multi-input and single-output (MISO) model based on system dynamics principles and the long short-term memory (LSTM) neural network model based on deep learning for flow simulation in the Yangtze River-Dongting Lake system. New hydrological insights for the region: The proposed MISO and LSTM models can achieve accurate flow simulation results using only recorded flow data and rainfall data in the Yangtze River-Dongting Lake system. Both models demonstrate significantly higher computational efficiency than traditional hydrodynamic models, the MISO model can complete simulation within one second, while the LSTM model has the higher Nash-Sutcliffe efficiency coefficients and the smaller relative errors during the training and validation periods, the peak discharge relative errors (PRE) is ranging from −1.40–1.81 % across twelve flood events. This study has provided a novel approach for flow simulation in the complex river-lake system.http://www.sciencedirect.com/science/article/pii/S2214581825002940Flow simulationDeep learningSystem modelComplex river-lake systemYangtze RiverDongting Lake |
| spellingShingle | Chenglong Li Shenglian Guo Zhen Cui Xin Xiang Flow simulation based on MISO and LSTM models in the Yangtze River-Dongting Lake System Journal of Hydrology: Regional Studies Flow simulation Deep learning System model Complex river-lake system Yangtze River Dongting Lake |
| title | Flow simulation based on MISO and LSTM models in the Yangtze River-Dongting Lake System |
| title_full | Flow simulation based on MISO and LSTM models in the Yangtze River-Dongting Lake System |
| title_fullStr | Flow simulation based on MISO and LSTM models in the Yangtze River-Dongting Lake System |
| title_full_unstemmed | Flow simulation based on MISO and LSTM models in the Yangtze River-Dongting Lake System |
| title_short | Flow simulation based on MISO and LSTM models in the Yangtze River-Dongting Lake System |
| title_sort | flow simulation based on miso and lstm models in the yangtze river dongting lake system |
| topic | Flow simulation Deep learning System model Complex river-lake system Yangtze River Dongting Lake |
| url | http://www.sciencedirect.com/science/article/pii/S2214581825002940 |
| work_keys_str_mv | AT chenglongli flowsimulationbasedonmisoandlstmmodelsintheyangtzeriverdongtinglakesystem AT shenglianguo flowsimulationbasedonmisoandlstmmodelsintheyangtzeriverdongtinglakesystem AT zhencui flowsimulationbasedonmisoandlstmmodelsintheyangtzeriverdongtinglakesystem AT xinxiang flowsimulationbasedonmisoandlstmmodelsintheyangtzeriverdongtinglakesystem |