Research on Monthly Runoff Forecast in Beijiang River Basin Based on Multi-model Ensemble Method
The accuracy of the monthly runoff forecast plays a fairly important role in aspects such as optimal allocation of water resources,flood control and drought relief in a basin,water dispatching,and power generation optimization of reservoir groups.The commonly used methods for the monthly runoff fore...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Pearl River
2022-01-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2022.06.006 |
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| _version_ | 1841535849468002304 |
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| author | ZHONG Yixuan LIAO Xiaolong QUAN Xujian YI Ling CHEN Yan LI Yuanyuan XUE Jiao |
| author_facet | ZHONG Yixuan LIAO Xiaolong QUAN Xujian YI Ling CHEN Yan LI Yuanyuan XUE Jiao |
| author_sort | ZHONG Yixuan |
| collection | DOAJ |
| description | The accuracy of the monthly runoff forecast plays a fairly important role in aspects such as optimal allocation of water resources,flood control and drought relief in a basin,water dispatching,and power generation optimization of reservoir groups.The commonly used methods for the monthly runoff forecast mainly include water balance models,mathematical statistics models,and artificial neural networks.Studies have shown that any single model cannot achieve the optimal monthly runoff forecast.Therefore,the multi-model ensemble method provides an effective way to eliminate model uncertainty and improve the accuracy of the monthly runoff forecast.Specifically,the research takes Pingshi,Lishi,Hengshi,and Shijiao stations in the Beijiang River Basin as the research object to analyze and compare the effects of the seasonal auto-regressive (SAR) model,two-parameter monthly water balance (TPMWB) model,and artificial neural network (ANN) model.Then,the multi-model ensemble method for the above-mentioned stations is proposed on the basis of the Bayesian model averaging (BMA) method.The research results reveal that compared with any of the three models,the multi-model ensemble method has significantly improved the accuracy of the monthly runoff forecast with a higher determination coefficient (DC) and a lower mean absolute percentage error (MAPE),and thus it can provide better support for decisions in dispatching in the basin. |
| format | Article |
| id | doaj-art-444663869f054b5091c4439ccb31efa6 |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2022-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-444663869f054b5091c4439ccb31efa62025-01-15T02:26:12ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352022-01-014347643057Research on Monthly Runoff Forecast in Beijiang River Basin Based on Multi-model Ensemble MethodZHONG YixuanLIAO XiaolongQUAN XujianYI LingCHEN YanLI YuanyuanXUE JiaoThe accuracy of the monthly runoff forecast plays a fairly important role in aspects such as optimal allocation of water resources,flood control and drought relief in a basin,water dispatching,and power generation optimization of reservoir groups.The commonly used methods for the monthly runoff forecast mainly include water balance models,mathematical statistics models,and artificial neural networks.Studies have shown that any single model cannot achieve the optimal monthly runoff forecast.Therefore,the multi-model ensemble method provides an effective way to eliminate model uncertainty and improve the accuracy of the monthly runoff forecast.Specifically,the research takes Pingshi,Lishi,Hengshi,and Shijiao stations in the Beijiang River Basin as the research object to analyze and compare the effects of the seasonal auto-regressive (SAR) model,two-parameter monthly water balance (TPMWB) model,and artificial neural network (ANN) model.Then,the multi-model ensemble method for the above-mentioned stations is proposed on the basis of the Bayesian model averaging (BMA) method.The research results reveal that compared with any of the three models,the multi-model ensemble method has significantly improved the accuracy of the monthly runoff forecast with a higher determination coefficient (DC) and a lower mean absolute percentage error (MAPE),and thus it can provide better support for decisions in dispatching in the basin.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2022.06.006multi-modelensemble forecastmonthly runoffBayesian model averagingBeijiang River |
| spellingShingle | ZHONG Yixuan LIAO Xiaolong QUAN Xujian YI Ling CHEN Yan LI Yuanyuan XUE Jiao Research on Monthly Runoff Forecast in Beijiang River Basin Based on Multi-model Ensemble Method Renmin Zhujiang multi-model ensemble forecast monthly runoff Bayesian model averaging Beijiang River |
| title | Research on Monthly Runoff Forecast in Beijiang River Basin Based on Multi-model Ensemble Method |
| title_full | Research on Monthly Runoff Forecast in Beijiang River Basin Based on Multi-model Ensemble Method |
| title_fullStr | Research on Monthly Runoff Forecast in Beijiang River Basin Based on Multi-model Ensemble Method |
| title_full_unstemmed | Research on Monthly Runoff Forecast in Beijiang River Basin Based on Multi-model Ensemble Method |
| title_short | Research on Monthly Runoff Forecast in Beijiang River Basin Based on Multi-model Ensemble Method |
| title_sort | research on monthly runoff forecast in beijiang river basin based on multi model ensemble method |
| topic | multi-model ensemble forecast monthly runoff Bayesian model averaging Beijiang River |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2022.06.006 |
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