An Intelligent Rockburst Prediction Model Based on Scorecard Methodology

Rockburst is a serious hazard in underground engineering, and accurate prediction of rockburst risk is challenging. To construct an intelligent prediction model of rockburst risk with interpretability and high accuracy, three binary scorecards predicting different risk levels of rockburst were const...

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Autores principales:	Honglei Wang, Zhenlei Li, Dazhao Song, Xueqiu He, Aleksei Sobolev, Majid Khan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	rockburst scorecard intelligence prediction interpretability class weights machine learning Mineralogy QE351-399.2
Acceso en línea:	https://doaj.org/article/2c9aa19e99604b91a8cadceefc489b1a
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spelling	oai:doaj.org-article:2c9aa19e99604b91a8cadceefc489b1a2021-11-25T18:26:55ZAn Intelligent Rockburst Prediction Model Based on Scorecard Methodology10.3390/min111112942075-163Xhttps://doaj.org/article/2c9aa19e99604b91a8cadceefc489b1a2021-11-01T00:00:00Zhttps://www.mdpi.com/2075-163X/11/11/1294https://doaj.org/toc/2075-163XRockburst is a serious hazard in underground engineering, and accurate prediction of rockburst risk is challenging. To construct an intelligent prediction model of rockburst risk with interpretability and high accuracy, three binary scorecards predicting different risk levels of rockburst were constructed using ChiMerge, evidence weight theory, and the logistic regression algorithm. An intelligent rockburst prediction model based on scorecard methodology (IRPSC) was obtained by integrating the three scorecards. The effects of hazard sample category weights on the missed alarm rate, false alarm rate, and accuracy of the IRPSC were analyzed. Results show that the accuracy, false alarm rate, and missed alarm rate of the IRPSC for rockburst prediction in riverside hydropower stations are 75%, 12.5%, and 12.5%, respectively. Setting higher hazard sample category weights can reduce the missed alarm rate of IRPSC, but it will lead to a higher false alarm rate. The IRPSC can adaptively adjust the threshold and weight value of the indicator and convert the abstract machine learning model into a tabular form, which overcomes the commonly black box problems of machine learning model, as well as is of great significance to the application of machine learning in rockburst risk prediction.Honglei WangZhenlei LiDazhao SongXueqiu HeAleksei SobolevMajid KhanMDPI AGarticlerockburstscorecardintelligence predictioninterpretabilityclass weightsmachine learningMineralogyQE351-399.2ENMinerals, Vol 11, Iss 1294, p 1294 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	rockburst scorecard intelligence prediction interpretability class weights machine learning Mineralogy QE351-399.2
spellingShingle	rockburst scorecard intelligence prediction interpretability class weights machine learning Mineralogy QE351-399.2 Honglei Wang Zhenlei Li Dazhao Song Xueqiu He Aleksei Sobolev Majid Khan An Intelligent Rockburst Prediction Model Based on Scorecard Methodology
description	Rockburst is a serious hazard in underground engineering, and accurate prediction of rockburst risk is challenging. To construct an intelligent prediction model of rockburst risk with interpretability and high accuracy, three binary scorecards predicting different risk levels of rockburst were constructed using ChiMerge, evidence weight theory, and the logistic regression algorithm. An intelligent rockburst prediction model based on scorecard methodology (IRPSC) was obtained by integrating the three scorecards. The effects of hazard sample category weights on the missed alarm rate, false alarm rate, and accuracy of the IRPSC were analyzed. Results show that the accuracy, false alarm rate, and missed alarm rate of the IRPSC for rockburst prediction in riverside hydropower stations are 75%, 12.5%, and 12.5%, respectively. Setting higher hazard sample category weights can reduce the missed alarm rate of IRPSC, but it will lead to a higher false alarm rate. The IRPSC can adaptively adjust the threshold and weight value of the indicator and convert the abstract machine learning model into a tabular form, which overcomes the commonly black box problems of machine learning model, as well as is of great significance to the application of machine learning in rockburst risk prediction.
format	article
author	Honglei Wang Zhenlei Li Dazhao Song Xueqiu He Aleksei Sobolev Majid Khan
author_facet	Honglei Wang Zhenlei Li Dazhao Song Xueqiu He Aleksei Sobolev Majid Khan
author_sort	Honglei Wang
title	An Intelligent Rockburst Prediction Model Based on Scorecard Methodology
title_short	An Intelligent Rockburst Prediction Model Based on Scorecard Methodology
title_full	An Intelligent Rockburst Prediction Model Based on Scorecard Methodology
title_fullStr	An Intelligent Rockburst Prediction Model Based on Scorecard Methodology
title_full_unstemmed	An Intelligent Rockburst Prediction Model Based on Scorecard Methodology
title_sort	intelligent rockburst prediction model based on scorecard methodology
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2c9aa19e99604b91a8cadceefc489b1a
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An Intelligent Rockburst Prediction Model Based on Scorecard Methodology

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