Research on Rockburst Prediction Classification Based on GA-XGB Model

Rockburst is a typical engineering geological disaster under the condition of high geostress. The rockburst classification and prediction are of great significance for the prevention and control of engineering geological disasters under the high geostress environment, and can reduce or even avoid th...

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Autores principales:	Xuebin Xie, Wei Jiang, Jiang Guo
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Rockburst prediction GA-XGB the ensemble algorithms confusion matrix Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/547e3c47bf4346a798d4065be2ebd7f8
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spelling	oai:doaj.org-article:547e3c47bf4346a798d4065be2ebd7f82021-11-19T00:06:50ZResearch on Rockburst Prediction Classification Based on GA-XGB Model2169-353610.1109/ACCESS.2021.3085745https://doaj.org/article/547e3c47bf4346a798d4065be2ebd7f82021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9446157/https://doaj.org/toc/2169-3536Rockburst is a typical engineering geological disaster under the condition of high geostress. The rockburst classification and prediction are of great significance for the prevention and control of engineering geological disasters under the high geostress environment, and can reduce or even avoid the loss of personnel, equipment, and property. Thus, the GA-XGB model for rockburst classification prediction is proposed in this paper. Specifically, the parameter search process of extreme gradient boosting (XGB) is optimized based on the genetic algorithm (GA), and 10-fold cross-validation is performed on the Dataset collected from literature research. There are 275 sets of complete data, including 6 parameters: <inline-formula> <tex-math notation="LaTeX">$\sigma _{\theta }$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\sigma _{\mathrm {c}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\sigma _{\mathrm {t}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\sigma _{\theta }/\sigma _{\mathrm {c}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\sigma _{\mathrm {c}}/\sigma _{\mathrm {t}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\text{W}_{\mathrm {et}}$ </tex-math></inline-formula>. Afterward, the impact of different indicator parameter combinations on the performance of the proposed model is explored and compared to existing machine learning models (such as SVM and XGB). Finally, the classification evaluation indexes (precision, recall, and F1 score) are calculated and compared to those obtained by several other algorithms. The results indicate that the method proposed in this paper performs better than other machine algorithms such as XGB, SVM, DT, and Bayes.Xuebin XieWei JiangJiang GuoIEEEarticleRockburst predictionGA-XGBthe ensemble algorithmsconfusion matrixElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 83993-84020 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Rockburst prediction GA-XGB the ensemble algorithms confusion matrix Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Rockburst prediction GA-XGB the ensemble algorithms confusion matrix Electrical engineering. Electronics. Nuclear engineering TK1-9971 Xuebin Xie Wei Jiang Jiang Guo Research on Rockburst Prediction Classification Based on GA-XGB Model
description	Rockburst is a typical engineering geological disaster under the condition of high geostress. The rockburst classification and prediction are of great significance for the prevention and control of engineering geological disasters under the high geostress environment, and can reduce or even avoid the loss of personnel, equipment, and property. Thus, the GA-XGB model for rockburst classification prediction is proposed in this paper. Specifically, the parameter search process of extreme gradient boosting (XGB) is optimized based on the genetic algorithm (GA), and 10-fold cross-validation is performed on the Dataset collected from literature research. There are 275 sets of complete data, including 6 parameters: <inline-formula> <tex-math notation="LaTeX">$\sigma _{\theta }$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\sigma _{\mathrm {c}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\sigma _{\mathrm {t}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\sigma _{\theta }/\sigma _{\mathrm {c}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\sigma _{\mathrm {c}}/\sigma _{\mathrm {t}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\text{W}_{\mathrm {et}}$ </tex-math></inline-formula>. Afterward, the impact of different indicator parameter combinations on the performance of the proposed model is explored and compared to existing machine learning models (such as SVM and XGB). Finally, the classification evaluation indexes (precision, recall, and F1 score) are calculated and compared to those obtained by several other algorithms. The results indicate that the method proposed in this paper performs better than other machine algorithms such as XGB, SVM, DT, and Bayes.
format	article
author	Xuebin Xie Wei Jiang Jiang Guo
author_facet	Xuebin Xie Wei Jiang Jiang Guo
author_sort	Xuebin Xie
title	Research on Rockburst Prediction Classification Based on GA-XGB Model
title_short	Research on Rockburst Prediction Classification Based on GA-XGB Model
title_full	Research on Rockburst Prediction Classification Based on GA-XGB Model
title_fullStr	Research on Rockburst Prediction Classification Based on GA-XGB Model
title_full_unstemmed	Research on Rockburst Prediction Classification Based on GA-XGB Model
title_sort	research on rockburst prediction classification based on ga-xgb model
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/547e3c47bf4346a798d4065be2ebd7f8
work_keys_str_mv	AT xuebinxie researchonrockburstpredictionclassificationbasedongaxgbmodel AT weijiang researchonrockburstpredictionclassificationbasedongaxgbmodel AT jiangguo researchonrockburstpredictionclassificationbasedongaxgbmodel
_version_	1718420608652410880

Research on Rockburst Prediction Classification Based on GA-XGB Model

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