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Keywords = LightGBM−TCN−RF model

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15 pages, 3893 KB  
Article
Research on Rockburst Risk Level Prediction Method Based on LightGBM−TCN−RF
by Li Ma, Jiajun Cai, Xinguan Dai and Ronghao Jia
Appl. Sci. 2022, 12(16), 8226; https://doi.org/10.3390/app12168226 - 17 Aug 2022
Cited by 11 | Viewed by 1804
Abstract
Rockburst hazards pose a severe threat to mine safety. To accurately predict the risk level of rockburst, a LightGBM−TCN−RF prediction model is proposed in this paper. The correlation coefficient heat map combined with the LightGBM feature selection algorithm is used to screen the [...] Read more.
Rockburst hazards pose a severe threat to mine safety. To accurately predict the risk level of rockburst, a LightGBM−TCN−RF prediction model is proposed in this paper. The correlation coefficient heat map combined with the LightGBM feature selection algorithm is used to screen the rockburst characteristic variables and establish rockburst predicted characteristic variables. Then, the TCN prediction model with a better prediction performance is selected to predict the rockburst characteristic variables at time t + 1. The RF classification model of rockburst risk level with a better classification effect is used to classify the risk level of rockburst characteristic variables at time t + 1. The comparison experiments show that the rockburst characteristic variables after screening allow a more accurate prediction. The overall RMSE and MAE of the TCN prediction model are 0.124 and 0.079, which are better than those of RNN, LSTM, and GRU by about 0.1–2.5%. The accuracy of the RF classification model for the rockburst risk level is 96.17%, which is about 20% higher than that of KNN and SVM, and the model accuracy is improved by 1.62% after parameter tuning by the PSO algorithm. The experimental results show that the LightGBM−TCN−RF model can better classify and predict rockburst risk levels at future moments, which has a certain reference value for rockburst monitoring and early warning. Full article
(This article belongs to the Special Issue AI-Based Image Processing)
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