Comparative Study for Daily Streamflow Simulation with Different Machine Learning Methods
Rainfall–runoff modeling has been of great importance for flood control and water resource management. However, the selection of hydrological models is challenging to obtain superior simulation performance especially with the rapid development of machine learning techniques. Three models under diffe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/6/1179 |
| _version_ | 1797608581908922368 |
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| author | Ruonan Hao Zhixu Bai |
| author_facet | Ruonan Hao Zhixu Bai |
| author_sort | Ruonan Hao |
| collection | DOAJ |
| description | Rainfall–runoff modeling has been of great importance for flood control and water resource management. However, the selection of hydrological models is challenging to obtain superior simulation performance especially with the rapid development of machine learning techniques. Three models under different categories of machine learning methods, including support vector regression (SVR), extreme gradient boosting (XGBoost), and the long-short term memory neural network (LSTM), were assessed for simulating daily runoff over a mountainous river catchment. The performances with different input scenarios were compared. Additionally, the joint multifractal spectra (JMS) method was implemented to evaluate the simulation performances during wet and dry seasons. The results show that: (1) LSTM always obtained a higher accuracy than XGBoost and SVR; (2) the impacts of the input variables were different for different machine learning methods, such as antecedent streamflow for XGBoost and rainfall for LSTM; (3) XGBoost showed a relatively high performance during dry seasons, and the classification of wet and dry seasons improved the simulation performance, especially for LSTM during dry seasons; (4) the JMS analysis indicated the advantages of a hybrid model combined with LSTM trained with wet-season data and XGBoost trained with dry-season data. |
| first_indexed | 2024-03-11T05:45:16Z |
| format | Article |
| id | doaj.art-e6f65546e5e24753a0c05ca5f3a23391 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T05:45:16Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-e6f65546e5e24753a0c05ca5f3a233912023-11-17T14:27:35ZengMDPI AGWater2073-44412023-03-01156117910.3390/w15061179Comparative Study for Daily Streamflow Simulation with Different Machine Learning MethodsRuonan Hao0Zhixu Bai1Academician Workstation in Anhui Province, Anhui University of Science and Technology, Huainan 232001, ChinaCollege of Civil Engineering and Architecture, Wenzhou University, Wenzhou 325035, ChinaRainfall–runoff modeling has been of great importance for flood control and water resource management. However, the selection of hydrological models is challenging to obtain superior simulation performance especially with the rapid development of machine learning techniques. Three models under different categories of machine learning methods, including support vector regression (SVR), extreme gradient boosting (XGBoost), and the long-short term memory neural network (LSTM), were assessed for simulating daily runoff over a mountainous river catchment. The performances with different input scenarios were compared. Additionally, the joint multifractal spectra (JMS) method was implemented to evaluate the simulation performances during wet and dry seasons. The results show that: (1) LSTM always obtained a higher accuracy than XGBoost and SVR; (2) the impacts of the input variables were different for different machine learning methods, such as antecedent streamflow for XGBoost and rainfall for LSTM; (3) XGBoost showed a relatively high performance during dry seasons, and the classification of wet and dry seasons improved the simulation performance, especially for LSTM during dry seasons; (4) the JMS analysis indicated the advantages of a hybrid model combined with LSTM trained with wet-season data and XGBoost trained with dry-season data.https://www.mdpi.com/2073-4441/15/6/1179LSTMXGBoostdifferent input scenariosjoint multifractal spectra |
| spellingShingle | Ruonan Hao Zhixu Bai Comparative Study for Daily Streamflow Simulation with Different Machine Learning Methods Water LSTM XGBoost different input scenarios joint multifractal spectra |
| title | Comparative Study for Daily Streamflow Simulation with Different Machine Learning Methods |
| title_full | Comparative Study for Daily Streamflow Simulation with Different Machine Learning Methods |
| title_fullStr | Comparative Study for Daily Streamflow Simulation with Different Machine Learning Methods |
| title_full_unstemmed | Comparative Study for Daily Streamflow Simulation with Different Machine Learning Methods |
| title_short | Comparative Study for Daily Streamflow Simulation with Different Machine Learning Methods |
| title_sort | comparative study for daily streamflow simulation with different machine learning methods |
| topic | LSTM XGBoost different input scenarios joint multifractal spectra |
| url | https://www.mdpi.com/2073-4441/15/6/1179 |
| work_keys_str_mv | AT ruonanhao comparativestudyfordailystreamflowsimulationwithdifferentmachinelearningmethods AT zhixubai comparativestudyfordailystreamflowsimulationwithdifferentmachinelearningmethods |