Application of Statistic Model and Backpropagation Neural Network to Analyzing and Forecasting Hydropower Dam Displacement
Main Article Content
Abstract
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.
References
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[4] J. Zou et.al., Dam deformation analysis based on BPNN merging models, Geo-spatial Information Science, 21(2) (2017) 49-157. https://doi.org/10.1080/10095020.2017.1386848
[5] Ministry of Science and Technology of Vietnam, TCVN 9399-2012: Buildings and Structures – Measuring Horizontal Displacement by Surveying Method, 2012 (in Vietnamese).
[6] Ministry of Science and Technology of Vietnam, TCVN 8215-2009: Hydraulic Structure – Major regulations on installation design observation equipment of water headworks, 2009 (in Vietnamese).
[7] Ministry of Science and Technology of Vietnam, TCVN 9360-2012: Technical Process of Settlement Monitoring of Civil and Industrial Building by Geometrical Levelling, 2012 (in Vietnamese).
[8] Ministry of Science and Technology of Vietnam, TCVN 9400-2012: Buildings and Tower Structure –Tilt Monitoring by Surveying Methods, 2012 (in Vietnamese).
[9] T. Khanh and L.D. Tinh, Applying a method of correlation analysis to estimate the movement of construction, Journal of Mining and Geology 4, 30 (2010) (in Vietnamese).
[10] L D. Tinh, Research the solutions to Increase the Effect of Deformation Monitoring in Vietnam (PhD thesis), Hanoi University of Mining and Geology, MS: 62.52.85.01, 2012 (in Vietnamese).
[11] Jonathan D. Cryer, Kung-Sik Chan, Time Series Analysis with Application in R, Springer-Verlag, New York, 2008.
[12] N.Q. Dong, N.T. Minh, Econometric, The Press of National Economics University, 2013 (in Vietnamese).
[13] D. Graupe, Principles of Artifcial Neural Networks, Advanced Series in Circuits and Systems 3rd Edition, vol. 7, World Scientific Publishing Co. Pte. Ltd., Singapore, 311–325, 2013.