Dam deformation analysis based on BPNN merging models |
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| Authors: | Jingui Zou Yangxuan Xiao Chinh Van Doan |
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| Institution: | 1. School of Geodesy and Geomatics, Wuhan University, Wuhan, China;2. Institute of Technique Special Engineering, Le Quy Don Technical University, Hanoi, Vietnam |
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| Abstract: | Hydropower has made a significant contribution to the economic development of Vietnam, thus it is important to monitor the safety of hydropower dams for the good of the country and the people. In this paper, dam horizontal displacement is analyzed and then forecasted using three methods: the multi-regression model, the seasonal integrated auto-regressive moving average (SARIMA) model and the back-propagation neural network (BPNN) merging models. The monitoring data of the Hoa Binh Dam in Vietnam, including horizontal displacement, time, reservoir water level, and air temperature, are used for the experiments. The results indicate that all of these three methods can approximately describe the trend of dam deformation despite their different forecast accuracies. Hence, their short-term forecasts can provide valuable references for the dam safety. |
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| Keywords: | Dam deformation analysis multi-regression model Back-propagation Neural Network (BPNN) Seasonal Integrated Auto-regressive Moving Average (SARIMA) model merging model |
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