Research on Detection and Restoration Methods of Basic Operation Data for Inter-Basin Water Diversion Projects
Inter-basin water diversion is an essential means to alleviate the contradiction between the supply and demand of water resources, and accurate hydraulic modelling guarantees smooth operation. However, due to the increasing complexity of water diversion methods, structures, water conservancy facilit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/21/11726 |
| _version_ | 1797632191689129984 |
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| author | Mengyao Lu Guitao Xu Xiaolian Liu |
| author_facet | Mengyao Lu Guitao Xu Xiaolian Liu |
| author_sort | Mengyao Lu |
| collection | DOAJ |
| description | Inter-basin water diversion is an essential means to alleviate the contradiction between the supply and demand of water resources, and accurate hydraulic modelling guarantees smooth operation. However, due to the increasing complexity of water diversion methods, structures, water conservancy facilities and equipment, it is difficult to obtain accurate and effective measured data to establish a model. Therefore, based on a data-driven method, this paper diagnoses and restores the important parameters of the water diversion projects, including the elevation of pipeline and water level data, which can be used to establish the adaptive hydraulic transition model of the water diversion projects. Firstly, the abnormal data of the elevation of pipeline were identified using expert data annotation and support vector classification (SVC), with the identification accuracy of abnormal data being as high as 91%. Then, the single and continuous abnormal data were restored using an interpolation method and multiple linear regression algorithm (MLR), and the restored data were found to be consistent with the judgment of expert knowledge. Secondly, K-medoids was used to classify the complex multi-dimensional water level data, combined with the 3-sigma method to identify the outliers in each class. The gradient boosting decision tree algorithm (GBDT) trained on normal data restored outliers in a predictive manner, and the mean absolute percentage error (MAPE) was 0.003%, 0.025% and 0.091% in each class, respectively, showing the best accuracy compared with other models. |
| first_indexed | 2024-03-11T11:34:28Z |
| format | Article |
| id | doaj.art-39e8a507c3aa41f19b5c992ca4bbf50d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T11:34:28Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-39e8a507c3aa41f19b5c992ca4bbf50d2023-11-10T14:58:31ZengMDPI AGApplied Sciences2076-34172023-10-0113211172610.3390/app132111726Research on Detection and Restoration Methods of Basic Operation Data for Inter-Basin Water Diversion ProjectsMengyao Lu0Guitao Xu1Xiaolian Liu2School of Civil Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Economics and Management, Hebei University of Technology, Tianjin 300401, ChinaCollege of Water Resource Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaInter-basin water diversion is an essential means to alleviate the contradiction between the supply and demand of water resources, and accurate hydraulic modelling guarantees smooth operation. However, due to the increasing complexity of water diversion methods, structures, water conservancy facilities and equipment, it is difficult to obtain accurate and effective measured data to establish a model. Therefore, based on a data-driven method, this paper diagnoses and restores the important parameters of the water diversion projects, including the elevation of pipeline and water level data, which can be used to establish the adaptive hydraulic transition model of the water diversion projects. Firstly, the abnormal data of the elevation of pipeline were identified using expert data annotation and support vector classification (SVC), with the identification accuracy of abnormal data being as high as 91%. Then, the single and continuous abnormal data were restored using an interpolation method and multiple linear regression algorithm (MLR), and the restored data were found to be consistent with the judgment of expert knowledge. Secondly, K-medoids was used to classify the complex multi-dimensional water level data, combined with the 3-sigma method to identify the outliers in each class. The gradient boosting decision tree algorithm (GBDT) trained on normal data restored outliers in a predictive manner, and the mean absolute percentage error (MAPE) was 0.003%, 0.025% and 0.091% in each class, respectively, showing the best accuracy compared with other models.https://www.mdpi.com/2076-3417/13/21/11726data miningwater diversion projectsdata-driven methodelevation of pipeline datawater level dataGBDT |
| spellingShingle | Mengyao Lu Guitao Xu Xiaolian Liu Research on Detection and Restoration Methods of Basic Operation Data for Inter-Basin Water Diversion Projects Applied Sciences data mining water diversion projects data-driven method elevation of pipeline data water level data GBDT |
| title | Research on Detection and Restoration Methods of Basic Operation Data for Inter-Basin Water Diversion Projects |
| title_full | Research on Detection and Restoration Methods of Basic Operation Data for Inter-Basin Water Diversion Projects |
| title_fullStr | Research on Detection and Restoration Methods of Basic Operation Data for Inter-Basin Water Diversion Projects |
| title_full_unstemmed | Research on Detection and Restoration Methods of Basic Operation Data for Inter-Basin Water Diversion Projects |
| title_short | Research on Detection and Restoration Methods of Basic Operation Data for Inter-Basin Water Diversion Projects |
| title_sort | research on detection and restoration methods of basic operation data for inter basin water diversion projects |
| topic | data mining water diversion projects data-driven method elevation of pipeline data water level data GBDT |
| url | https://www.mdpi.com/2076-3417/13/21/11726 |
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