|本期目录/Table of Contents|

[1]戴健非,杨鹏,王昕宇.基于BP神经网络和SVR的Fund?o尾矿坝排水数据预测对比研究[J].中国安全生产科学技术,2019,15(3):92-97.[doi:10.11731/j.issn.1673-193x.2019.03.015]
　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]
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基于BP神经网络和SVR的Fund?o尾矿坝排水数据预测对比研究
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
15
期数:
2019年3期
页码:
92-97
栏目:
职业安全卫生管理与技术
出版日期:
2019-03-31

文章信息/Info

Title:
Comparative study on prediction of water flow data for Fundo dam based on neural network and SVR
文章编号:
1673-193X(2019)-03-0092-06
作者:
戴健非1; 杨鹏1; 2; 王昕宇1
（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)
Author(s):
DAI Jianfei1; YANG Peng1; 2; WANG 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)
关键词:
BP神经网络; SVR; 排水数据; 早期预警; 尾矿库
Keywords:
BP neural network;  support vector regression (SVR);  water flow data;  early warning;  tailing pond
分类号:
X936；TD745
DOI:
10.11731/j.issn.1673-193x.2019.03.015
文献标志码:
A
摘要:
针对尾矿库运行过程中安全预警问题，选取2015年巴西Samarco铁矿溃坝事故案例，研究BP神经网络和SVR方法在排水数据预测的适用性。综合分析了排水数据的复杂且非线性的特点，以库水位、降雨量和干滩长度为输入特征，采用上述2个模型对尾矿坝排水数据进行预测。研究结果表明：基于BP神经网络预测结果的最大相对误差不高于4.35%；基于SVR算法的最大相对误差不高于9.21%；Fundo坝的排水预测结果是可行的，BP神经网络的预测精度更高，而SVR模型的运算速度更快。研究结果可为矿山安全工作的快速响应和溃坝预警提供信息支撑和参考依据。
Abstract:
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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备注/Memo

备注/Memo:

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