Bui Thi Kieu Trinh, Xiao Yangxuan, Chinh Van Doan, Do Xuan Khanh, Mai Dinh Sinh
{"title":"统计模型与反向传播神经网络在水电站大坝位移分析与预测中的应用","authors":"Bui Thi Kieu Trinh, Xiao Yangxuan, Chinh Van Doan, Do Xuan Khanh, Mai Dinh Sinh","doi":"10.25073/2588-1094/VNUEES.4529","DOIUrl":null,"url":null,"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.","PeriodicalId":247618,"journal":{"name":"VNU Journal of Science: Earth and Environmental Sciences","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Application of Statistic Model and Backpropagation Neural Network to Analyzing and Forecasting Hydropower Dam Displacement\",\"authors\":\"Bui Thi Kieu Trinh, Xiao Yangxuan, Chinh Van Doan, Do Xuan Khanh, Mai Dinh Sinh\",\"doi\":\"10.25073/2588-1094/VNUEES.4529\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":247618,\"journal\":{\"name\":\"VNU Journal of Science: Earth and Environmental Sciences\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-03-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"VNU Journal of Science: Earth and Environmental Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.25073/2588-1094/VNUEES.4529\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"VNU Journal of Science: Earth and Environmental Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25073/2588-1094/VNUEES.4529","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of Statistic Model and Backpropagation Neural Network to Analyzing and Forecasting Hydropower Dam Displacement
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.