Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect

• Published in: Water Science and Engineering Vol. 11; no. 4; pp. 344 - 354
• Main Authors: Wang, Shao-wei, Xu, Ying-li, Gu, Chong-shi, Bao, Teng-fei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2018; School 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%School 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; Elsevier
• Subjects: Base flow; case studies; Daily variation; Dam seepage monitoring model; dams (hydrology); environmental factors; monitoring; normal distribution; Piezometric tube water level; rain; seepage; Sensitivity analysis; Support vector machine (SVM); support vector machines; Time lag effect; time series analysis; wavelet; Support vector machine (SVM); Base flow; Sensitivity analysis; Piezometric tube water level; Daily variation; Dam seepage monitoring model; Time lag effect
• ISSN: 1674-2370
• DOI: 10.1016/j.wse.2018.12.004

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.