|本期目录/Table of Contents|

 DAI Jianfei,YANG Peng,WANG Xinyu.Comparative study on prediction of water flow data for Fundo dam based on neural network and SVR[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2019,15(3):92-97.[doi:10.11731/j.issn.1673-193x.2019.03.015]





Comparative study on prediction of water flow data for Fundo dam based on neural network and SVR
(1.北京联合大学 北京市信息服务工程重点实验室,北京 100101; 2.北京科技大学 土木与资源工程学院,北京 100083)
DAI Jianfei1YANG Peng12WANG Xinyu1
(1. Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China;2. School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)
BP neural network support vector regression (SVR) water flow data early warning tailing pond
In order to solve the problem of safety early warning in the operation process of tailing pond, the case of dam failure accident of Samarco iron mine in Brazil in 2015 was selected to study the applicability of BP neural network and support vector regression (SVR) method in the prediction of water flow data. The characteristics of complexity and nonlinearity of the water flow data were analyzed comprehensively, and the water flow data of tailing dam were predicted by using the above two models with taking the pond water level, rainfall and dry beach length as the input characteristics. The results showed that the maximum relative error of the prediction results based on BP neural network was not higher than 4.35%, and the maximum relative error based on SVR algorithm was not higher than 9.21%. The prediction results of water flow for the Fundo dam were feasible, and the prediction accuracy of BP neural network was higher, while the calculation speed of SVR model was faster. The results can provide the information support and reference basis for the rapid response and the early warning of dam failure in the safety work of mine.


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收稿日期: 2018-11-22
基金项目: 国家重点研发计划课题(2017YFC0804600);国家自然科学基金项目(51774045);北京联合大学研究生资助项目
作者简介: 戴健非,硕士研究生,主要研究方向为尾矿坝安全工程。
通信作者: 杨鹏,博士,教授,主要研究方向为矿业及安全工程。
更新日期/Last Update: 2019-04-15