Bui Thi Kien Trinh, Xiao Yangxuan, Chinh Van Doan, Do Xuan Khanh, Tran The Viet, Mai Dinh Sinh

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Horizontal displacement of Hoa Binh dam in operation phase was analyzed and then forecasted by using three methods: the multi-regression model (MTR), the Seasonal Integrated Auto-regressive Moving Average (SARIMA) and the Back-propagation Neural Network (BPNN). The monitoring data of the Hoa Binh Dam in 137 periods, including horizontal displacement, time, reservoir water level and air temperature were used for the experiments. The results indicated that all of these three methods could describe the real trend of dam deformation and achieve the required accuracy in short-term forecast up to 9 months. In addition, forecast results of BPNN had the highest stability and accuracy




Keywords: Horizontal displacement, Multi-regression model, Seasonal ARIMA, Back-propagation neural network.


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