An integrated framework for prediction and sensitivity analysis of water levels in front of pumping stations
Study region: The South-to-North Water Diversion Eastern Route Project section from the Nansihu-Dongpinghu pumping station cluster.Study focus: An integrated framework for prediction and sensitivity analysis of water levels in front of pumping stations is proposed to obtain more accurate predictive...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Journal of Hydrology: Regional Studies |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581824004683 |
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| _version_ | 1832591750974144512 |
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| author | Weilin Wang Guoqing Sang Qiang Zhao Yang Liu Longbin Lu Guangwen Shao |
| author_facet | Weilin Wang Guoqing Sang Qiang Zhao Yang Liu Longbin Lu Guangwen Shao |
| author_sort | Weilin Wang |
| collection | DOAJ |
| description | Study region: The South-to-North Water Diversion Eastern Route Project section from the Nansihu-Dongpinghu pumping station cluster.Study focus: An integrated framework for prediction and sensitivity analysis of water levels in front of pumping stations is proposed to obtain more accurate predictive surrogate models and to simplify surrogate model inputs. The results show that among the three different water transport models, the Firefly-Support Vector Machine model has a smaller mean absolute error (<2.38 %), root mean square error (<4.76 %), and mean absolute percentage error (<0.07 %) with higher linear correlation (>0.85). The Firefly-Support Vector Machine model is more suitable for water level prediction than other models. The water level in front of the target pumping station and the t-ahead flow were the most sensitive parameters, and the longer the foresight period, the higher the importance.New hydrological insight for the region: Three water transportation modes are proposed according to the characteristics of regional hydrological connectivity in the long-distance water transportation system. This enables the water level prediction surrogate model to adapt to the complex connectivity of pumping stations and lakes in the region, improving the accuracy of water level prediction. Subsequently, the parameter sensitivity of the water level prediction surrogate model for each water transport mode was also tested. |
| format | Article |
| id | doaj-art-1e2f7fb7b3794099a8ba51083d6b6b80 |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-1e2f7fb7b3794099a8ba51083d6b6b802025-01-22T05:42:06ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-02-0157102119An integrated framework for prediction and sensitivity analysis of water levels in front of pumping stationsWeilin Wang0Guoqing Sang1Qiang Zhao2Yang Liu3Longbin Lu4Guangwen Shao5School of Water Conservancy and Environment, University of Jinan, Jinan, ChinaCorresponding author.; School of Water Conservancy and Environment, University of Jinan, Jinan, ChinaSchool of Water Conservancy and Environment, University of Jinan, Jinan, ChinaSchool of Water Conservancy and Environment, University of Jinan, Jinan, ChinaSchool of Water Conservancy and Environment, University of Jinan, Jinan, ChinaSchool of Water Conservancy and Environment, University of Jinan, Jinan, ChinaStudy region: The South-to-North Water Diversion Eastern Route Project section from the Nansihu-Dongpinghu pumping station cluster.Study focus: An integrated framework for prediction and sensitivity analysis of water levels in front of pumping stations is proposed to obtain more accurate predictive surrogate models and to simplify surrogate model inputs. The results show that among the three different water transport models, the Firefly-Support Vector Machine model has a smaller mean absolute error (<2.38 %), root mean square error (<4.76 %), and mean absolute percentage error (<0.07 %) with higher linear correlation (>0.85). The Firefly-Support Vector Machine model is more suitable for water level prediction than other models. The water level in front of the target pumping station and the t-ahead flow were the most sensitive parameters, and the longer the foresight period, the higher the importance.New hydrological insight for the region: Three water transportation modes are proposed according to the characteristics of regional hydrological connectivity in the long-distance water transportation system. This enables the water level prediction surrogate model to adapt to the complex connectivity of pumping stations and lakes in the region, improving the accuracy of water level prediction. Subsequently, the parameter sensitivity of the water level prediction surrogate model for each water transport mode was also tested.http://www.sciencedirect.com/science/article/pii/S2214581824004683Back propagation neural networkExtreme learning machineFirefly-Support Vector MachineInput parametersRandom ForestSensitivity analysis |
| spellingShingle | Weilin Wang Guoqing Sang Qiang Zhao Yang Liu Longbin Lu Guangwen Shao An integrated framework for prediction and sensitivity analysis of water levels in front of pumping stations Journal of Hydrology: Regional Studies Back propagation neural network Extreme learning machine Firefly-Support Vector Machine Input parameters Random Forest Sensitivity analysis |
| title | An integrated framework for prediction and sensitivity analysis of water levels in front of pumping stations |
| title_full | An integrated framework for prediction and sensitivity analysis of water levels in front of pumping stations |
| title_fullStr | An integrated framework for prediction and sensitivity analysis of water levels in front of pumping stations |
| title_full_unstemmed | An integrated framework for prediction and sensitivity analysis of water levels in front of pumping stations |
| title_short | An integrated framework for prediction and sensitivity analysis of water levels in front of pumping stations |
| title_sort | integrated framework for prediction and sensitivity analysis of water levels in front of pumping stations |
| topic | Back propagation neural network Extreme learning machine Firefly-Support Vector Machine Input parameters Random Forest Sensitivity analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2214581824004683 |
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