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Monitoring and Warning Methods of Tailings Reservoir using BP Neural Network

Volume 14, Number 6, June 2018, pp. 1171-1180
DOI: 10.23940/ijpe.18.06.p8.11711180

Tianyong Wu­a, Chunyuan Zhangb, and Yunsheng Zhaoa

aFaculty of Engineering, China University of Geosciences, Wuhan, 430074, China
bSchool of Computer Science, China University of Geosciences, Wuhan, 430074, China

(Submitted on February 27, 2018; Revised on April 1, 2018; Accepted on May 21, 2018)


The tailings reservoir is a major hazard source with high potential energy, which may cause artificial debris flow. The stability of the tailings reservoir is extremely important to the normal operation of the mining enterprises and the safety of people's lives and property. In order to reduce the risk of a tailings accident, a multivariate linear regression model, a BP neural network and a regression analysis model optimized by genetic algorithm are established in this article to discuss the monitoring and warning method of the tailings reservoir. It takes the safety monitoring data of the Huangmailing tailings as an example to make a comparison of three forecasting models by taking fitness, simulating capability of initial data and the predicting ability of new data into consideration. The results of the experiment show that the BP neural network forecasting model is better able to predict safety monitoring data over the other two models. The predicting ability of the regression analysis model optimized by genetic algorithm is better than the forecasting capability of the multivariate linear regression model.


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