Impact Assessment of Coupling Mode of Hydrological Model and Machine Learning Model on Runoff Simulation: A Case of Washington
The inherent uncertainties in traditional hydrological models present significant challenges for accurately simulating runoff. Combining machine learning models with traditional hydrological models is an essential approach to enhancing the runoff modeling capabilities of hydrological models. However...
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MDPI AG
2024-12-01
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| author | Junqi Zhang Jing Li Huiyizhe Zhao Wen Wang Na Lv Bowen Zhang Yue Liu Xinyu Yang Mengjing Guo Yuhao Dong |
| author_facet | Junqi Zhang Jing Li Huiyizhe Zhao Wen Wang Na Lv Bowen Zhang Yue Liu Xinyu Yang Mengjing Guo Yuhao Dong |
| author_sort | Junqi Zhang |
| collection | DOAJ |
| description | The inherent uncertainties in traditional hydrological models present significant challenges for accurately simulating runoff. Combining machine learning models with traditional hydrological models is an essential approach to enhancing the runoff modeling capabilities of hydrological models. However, research on the impact of mixed models on runoff simulation capability is limited. Therefore, this study uses the traditional hydrological model Simplified Daily Hydrological Model (SIMHYD) and the machine learning model Long Short Term Memory (LSTM) to construct two coupled models: a direct coupling model and a dynamically improved predictive validity hybrid model. These models were evaluated using the US CAMELS dataset to assess the impact of the two model combination methods on runoff modeling capabilities. The results indicate that the runoff modeling capabilities of both combination methods were improved compared to individual models, with the combined forecasting model for dynamic prediction effectiveness (DPE) demonstrating the optimal modeling capability. Compared with LSTM, the mixed model showed a median increase of 12.8% in Nash Sutcliffe efficiency (NSE) of daily runoff during the validation period, and a 12.5% increase compared to SIMHYD. In addition, compared with the LSTM model, the median Nash Sutcliffe efficiency (NSE) of the hybrid model simulating high flow results increased by 23.6%, and compared with SIMHYD, it increased by 28.4%. At the same time, the stability of the hybrid model simulating low flow was significantly improved. In performance testing involving varying training period lengths, the DPE model trained for 12 years exhibited the best performance, showing a 3.5% and 1.5% increase in the median NSE compared to training periods of 6 years and 18 years, respectively. |
| format | Article |
| id | doaj-art-4a46f8dbc7494d6c9ecaa8eaeee9fa71 |
| institution | DOAJ |
| issn | 2073-4433 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Atmosphere |
| spelling | doaj-art-4a46f8dbc7494d6c9ecaa8eaeee9fa712025-08-20T02:53:43ZengMDPI AGAtmosphere2073-44332024-12-011512146110.3390/atmos15121461Impact Assessment of Coupling Mode of Hydrological Model and Machine Learning Model on Runoff Simulation: A Case of WashingtonJunqi Zhang0Jing Li1Huiyizhe Zhao2Wen Wang3Na Lv4Bowen Zhang5Yue Liu6Xinyu Yang7Mengjing Guo8Yuhao Dong9State Key Laboratory of Eco-Hydrological in Northwest Arid Region, Xi’an University of Technology, Xi’an University of Technology, Xi’an 710018, ChinaState Key Laboratory of Eco-Hydrological in Northwest Arid Region, Xi’an University of Technology, Xi’an University of Technology, Xi’an 710018, ChinaState Key Laboratory of Eco-Hydrological in Northwest Arid Region, Xi’an University of Technology, Xi’an University of Technology, Xi’an 710018, ChinaState Key Laboratory of Eco-Hydrological in Northwest Arid Region, Xi’an University of Technology, Xi’an University of Technology, Xi’an 710018, ChinaState Key Laboratory of Eco-Hydrological in Northwest Arid Region, Xi’an University of Technology, Xi’an University of Technology, Xi’an 710018, ChinaState Key Laboratory of Eco-Hydrological in Northwest Arid Region, Xi’an University of Technology, Xi’an University of Technology, Xi’an 710018, ChinaState Key Laboratory of Eco-Hydrological in Northwest Arid Region, Xi’an University of Technology, Xi’an University of Technology, Xi’an 710018, ChinaState Key Laboratory of Eco-Hydrological in Northwest Arid Region, Xi’an University of Technology, Xi’an University of Technology, Xi’an 710018, ChinaState Key Laboratory of Eco-Hydrological in Northwest Arid Region, Xi’an University of Technology, Xi’an University of Technology, Xi’an 710018, ChinaState Key Laboratory of Eco-Hydrological in Northwest Arid Region, Xi’an University of Technology, Xi’an University of Technology, Xi’an 710018, ChinaThe inherent uncertainties in traditional hydrological models present significant challenges for accurately simulating runoff. Combining machine learning models with traditional hydrological models is an essential approach to enhancing the runoff modeling capabilities of hydrological models. However, research on the impact of mixed models on runoff simulation capability is limited. Therefore, this study uses the traditional hydrological model Simplified Daily Hydrological Model (SIMHYD) and the machine learning model Long Short Term Memory (LSTM) to construct two coupled models: a direct coupling model and a dynamically improved predictive validity hybrid model. These models were evaluated using the US CAMELS dataset to assess the impact of the two model combination methods on runoff modeling capabilities. The results indicate that the runoff modeling capabilities of both combination methods were improved compared to individual models, with the combined forecasting model for dynamic prediction effectiveness (DPE) demonstrating the optimal modeling capability. Compared with LSTM, the mixed model showed a median increase of 12.8% in Nash Sutcliffe efficiency (NSE) of daily runoff during the validation period, and a 12.5% increase compared to SIMHYD. In addition, compared with the LSTM model, the median Nash Sutcliffe efficiency (NSE) of the hybrid model simulating high flow results increased by 23.6%, and compared with SIMHYD, it increased by 28.4%. At the same time, the stability of the hybrid model simulating low flow was significantly improved. In performance testing involving varying training period lengths, the DPE model trained for 12 years exhibited the best performance, showing a 3.5% and 1.5% increase in the median NSE compared to training periods of 6 years and 18 years, respectively.https://www.mdpi.com/2073-4433/15/12/1461runoff simulationhydrological modelmachine learningSIMHYDLSTMUS CAMELS dataset |
| spellingShingle | Junqi Zhang Jing Li Huiyizhe Zhao Wen Wang Na Lv Bowen Zhang Yue Liu Xinyu Yang Mengjing Guo Yuhao Dong Impact Assessment of Coupling Mode of Hydrological Model and Machine Learning Model on Runoff Simulation: A Case of Washington Atmosphere runoff simulation hydrological model machine learning SIMHYD LSTM US CAMELS dataset |
| title | Impact Assessment of Coupling Mode of Hydrological Model and Machine Learning Model on Runoff Simulation: A Case of Washington |
| title_full | Impact Assessment of Coupling Mode of Hydrological Model and Machine Learning Model on Runoff Simulation: A Case of Washington |
| title_fullStr | Impact Assessment of Coupling Mode of Hydrological Model and Machine Learning Model on Runoff Simulation: A Case of Washington |
| title_full_unstemmed | Impact Assessment of Coupling Mode of Hydrological Model and Machine Learning Model on Runoff Simulation: A Case of Washington |
| title_short | Impact Assessment of Coupling Mode of Hydrological Model and Machine Learning Model on Runoff Simulation: A Case of Washington |
| title_sort | impact assessment of coupling mode of hydrological model and machine learning model on runoff simulation a case of washington |
| topic | runoff simulation hydrological model machine learning SIMHYD LSTM US CAMELS dataset |
| url | https://www.mdpi.com/2073-4433/15/12/1461 |
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