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Mining Technology and Mine Management

Prediction Model of Soil Dump Stability Based on Principal Component Analysis and PSO-ELM Algorithm

  • Feng GAO ,
  • Xiaodong WU ,
  • Keping ZHOU
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  • School of Resources and Safety Engineering,Central South University,Changsha 410083,Hunan,China

Received date: 2020-09-21

  Revised date: 2021-04-13

  Online published: 2021-12-17

Abstract

Aiming at the stability analysis of dump slope,a PCA-PSO-ELM dump slope stability prediction model is proposed in this paper,which uses principal component analysis method to reduce data redundancy and particle swarm optimization algorithm to optimize the weight threshold of extreme learning machine. Eight prediction indexes of dump stability were determined in this model,including soil cohesion,internal friction angle,dump slope angle,foundation bearing capacity,seismic intensity,rainfall and snowfall conditions,dumping technology and random mining and digging conditions.According to 100 groups of corresponding dump data,training time,RMSE value and determination coefficient R2 were used to evaluate and compare the validity of prediction results of PCA-PSO-ELM model,BP neural network model,ELM model and PSO-ELM model.The research results show that as the input variable to train and test the PSO-ELM network model,the dump stability sample data processed by PCA dimensionality reduction,made predicted value very close to the real value.The prediction accuracy and efficiency are not only higher than the ELM algorithm,but also far better than the traditional BP neural network algorithm.Compared with the PSO-ELM model without PCA treatment,the PSO-ELM model optimized by PCA method can significantly shorten the calculation time on the basis of little difference in efficiency,which proves that the method has certain practical value.

Cite this article

Feng GAO , Xiaodong WU , Keping ZHOU . Prediction Model of Soil Dump Stability Based on Principal Component Analysis and PSO-ELM Algorithm[J]. Gold Science and Technology, 2021 , 29(5) : 658 -668 . DOI: 10.11872/j.issn.1005-2518.2021.05.168

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