河南城建学院学报2023，Vol.32Issue(6)：89-95,7.DOI:10.14140/j.cnki.hncjxb.2023.06.013
基于WOA优化Elman神经网络的露天矿边坡位移预测
Prediction of open-pit mine slope displacement based on WOA-Elman neural network
摘要
Abstract
Slope deformation and instability of open-pit mines will seriously threaten the safety of mine produc-tion.In order to improve the accuracy and reliability of the open-pit mine slope deformation prediction,the Whale Optimization Algorithm(WOA)was used to optimize the prediction model of the Elman neural network to predict the open-pit mine slope displacement.According to the characteristics of the Elman neural network,the optimal Elman neural network topology is constructed by optimizing the number of input layer nodes,the number of hidden layer nodes,and the transfer function parameters,and the weights and thresholds of the El-man neural network are optimized through WOA to enhance training speed and global optimization capabilities of the Elman neural network.Taking the slope monitoring data of Fushun West Open-pit Mine as an example,this prediction model is used for dynamic prediction and compared with the classic BP and Elman neural net-works.The results show that:the maximum relative error and the average absolute error of the WOA-Elman model prediction results are 0.018％and 0.146 mm,the model has fast convergence speed and strong stabili-ty,which can provide an effective way to predict slope deformation in mining areas.关键词
Elman神经网络/露天矿边坡/位移预测/鲸鱼算法Key words
Elman neural network/open-pit mine slop/slope displacement prediction/Whale Optimization Al-gorithm分类
信息技术与安全科学引用本文复制引用
高宁,戚鑫鑫,杨逸飞,高彩云..基于WOA优化Elman神经网络的露天矿边坡位移预测[J].河南城建学院学报,2023,32(6):89-95,7.基金项目
河南省科技攻关项目(222102320414,232102321013) （222102320414,232102321013）
