Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect
Affected by external environmental factors and evolution of dam performance, dam seepage behavior shows nonlinear time-varying characteristics. In this study, to predict and evaluate the long-term development trend and short-term fluctuation of the dam seepage behavior, two monitoring models were de...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2018-10-01
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| Series: | Water Science and Engineering |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674237018300991 |
| _version_ | 1818146294558883840 |
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| author | Shao-wei Wang Ying-li Xu Chong-shi Gu Teng-fei Bao |
| author_facet | Shao-wei Wang Ying-li Xu Chong-shi Gu Teng-fei Bao |
| author_sort | Shao-wei Wang |
| collection | DOAJ |
| description | Affected by external environmental factors and evolution of dam performance, dam seepage behavior shows nonlinear time-varying characteristics. In this study, to predict and evaluate the long-term development trend and short-term fluctuation of the dam seepage behavior, two monitoring models were developed, one for the base flow effect and one for daily variation of dam seepage elements. In the first model, to avoid the influence of the time lag effect on the evaluation of seepage variation with the time effect component of seepage elements, the base values of the seepage element and the reservoir water level were extracted using the wavelet multi-resolution analysis method, and the time effect component was separated by the established base flow effect monitoring model. For the development of the daily variation monitoring model for dam seepage elements, all the previous factors, of which the measured time series prior to the dam seepage element monitoring time may have certain influence on the monitored results, were considered. Those factors that were positively correlated with the analyzed seepage element were initially considered to be the support vector machine (SVM) model input factors, and then the SVM kernel function-based sensitivity analysis was performed to optimize the input factor set and establish the optimized daily variation SVM model. The efficiency and rationality of the two models were verified by case studies of the water level of two piezometric tubes buried under the slope of a concrete gravity dam. Sensitivity analysis of the optimized SVM model shows that the influences of the daily variation of the upstream reservoir water level and rainfall on the daily variation of piezometric tube water level are processes subject to normal distribution. Keywords: Dam seepage monitoring model, Time lag effect, Support vector machine (SVM), Sensitivity analysis, Base flow, Daily variation, Piezometric tube water level |
| first_indexed | 2024-12-11T12:17:04Z |
| format | Article |
| id | doaj.art-8d84a141836a46cab8df216097ca0d3d |
| institution | Directory Open Access Journal |
| issn | 1674-2370 |
| language | English |
| last_indexed | 2024-12-11T12:17:04Z |
| publishDate | 2018-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Water Science and Engineering |
| spelling | doaj.art-8d84a141836a46cab8df216097ca0d3d2022-12-22T01:07:37ZengElsevierWater Science and Engineering1674-23702018-10-01114344354Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effectShao-wei Wang0Ying-li Xu1Chong-shi Gu2Teng-fei Bao3School of Environmental and Safety Engineering, Changzhou University, Changzhou 213164, China; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; Corresponding author.School of Environmental and Safety Engineering, Changzhou University, Changzhou 213164, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, ChinaAffected by external environmental factors and evolution of dam performance, dam seepage behavior shows nonlinear time-varying characteristics. In this study, to predict and evaluate the long-term development trend and short-term fluctuation of the dam seepage behavior, two monitoring models were developed, one for the base flow effect and one for daily variation of dam seepage elements. In the first model, to avoid the influence of the time lag effect on the evaluation of seepage variation with the time effect component of seepage elements, the base values of the seepage element and the reservoir water level were extracted using the wavelet multi-resolution analysis method, and the time effect component was separated by the established base flow effect monitoring model. For the development of the daily variation monitoring model for dam seepage elements, all the previous factors, of which the measured time series prior to the dam seepage element monitoring time may have certain influence on the monitored results, were considered. Those factors that were positively correlated with the analyzed seepage element were initially considered to be the support vector machine (SVM) model input factors, and then the SVM kernel function-based sensitivity analysis was performed to optimize the input factor set and establish the optimized daily variation SVM model. The efficiency and rationality of the two models were verified by case studies of the water level of two piezometric tubes buried under the slope of a concrete gravity dam. Sensitivity analysis of the optimized SVM model shows that the influences of the daily variation of the upstream reservoir water level and rainfall on the daily variation of piezometric tube water level are processes subject to normal distribution. Keywords: Dam seepage monitoring model, Time lag effect, Support vector machine (SVM), Sensitivity analysis, Base flow, Daily variation, Piezometric tube water levelhttp://www.sciencedirect.com/science/article/pii/S1674237018300991 |
| spellingShingle | Shao-wei Wang Ying-li Xu Chong-shi Gu Teng-fei Bao Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect Water Science and Engineering |
| title | Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect |
| title_full | Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect |
| title_fullStr | Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect |
| title_full_unstemmed | Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect |
| title_short | Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect |
| title_sort | monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect |
| url | http://www.sciencedirect.com/science/article/pii/S1674237018300991 |
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