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Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis

In order to predict gas explosion disasters rapidly and accurately, this study utilizes real-time data collected from the intelligent mining system, including mine safety monitoring, personnel positioning, and video surveillance. Firstly, the coal mine disaster system is decomposed into sub-systems...

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Detalles Bibliográficos
Autores principales: Li, Hongxia, Zhang, Yiru, Yang, Wanli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10621839/
https://www.ncbi.nlm.nih.gov/pubmed/37917652
http://dx.doi.org/10.1371/journal.pone.0293814
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author Li, Hongxia
Zhang, Yiru
Yang, Wanli
author_facet Li, Hongxia
Zhang, Yiru
Yang, Wanli
author_sort Li, Hongxia
collection PubMed
description In order to predict gas explosion disasters rapidly and accurately, this study utilizes real-time data collected from the intelligent mining system, including mine safety monitoring, personnel positioning, and video surveillance. Firstly, the coal mine disaster system is decomposed into sub-systems of disaster-causing factors, disaster-prone environments, and vulnerable bodies, establishing an early warning index system for gas explosion disasters. Then, a training set is randomly selected from known coal mine samples, and the training sample set is processed and analyzed using Matlab software. Subsequently, a training model based on the random forest classification algorithm is constructed, and the model is optimized using two parameters, Mtry and Ntree. Finally, the constructed random forest-based gas explosion early warning model is compared with a classification model based on the support vector machine (SVM) algorithm. Specific coal mine case studies are conducted to verify the applicability of the optimized random forest algorithm. The experimental results demonstrate that: The optimized random forest model has achieved 100% accuracy in predicting gas explosion disaster of coal mines, while the accuracy of SVM model is only 75%. The optimized model also shows lower model error and relative error, which proves its high performance in early warning of coal mine gas explosion. This study innovatively combines intelligent mining system with multidimensional data analysis, which provides a new method for coal mine safety management.
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spelling pubmed-106218392023-11-03 Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis Li, Hongxia Zhang, Yiru Yang, Wanli PLoS One Research Article In order to predict gas explosion disasters rapidly and accurately, this study utilizes real-time data collected from the intelligent mining system, including mine safety monitoring, personnel positioning, and video surveillance. Firstly, the coal mine disaster system is decomposed into sub-systems of disaster-causing factors, disaster-prone environments, and vulnerable bodies, establishing an early warning index system for gas explosion disasters. Then, a training set is randomly selected from known coal mine samples, and the training sample set is processed and analyzed using Matlab software. Subsequently, a training model based on the random forest classification algorithm is constructed, and the model is optimized using two parameters, Mtry and Ntree. Finally, the constructed random forest-based gas explosion early warning model is compared with a classification model based on the support vector machine (SVM) algorithm. Specific coal mine case studies are conducted to verify the applicability of the optimized random forest algorithm. The experimental results demonstrate that: The optimized random forest model has achieved 100% accuracy in predicting gas explosion disaster of coal mines, while the accuracy of SVM model is only 75%. The optimized model also shows lower model error and relative error, which proves its high performance in early warning of coal mine gas explosion. This study innovatively combines intelligent mining system with multidimensional data analysis, which provides a new method for coal mine safety management. Public Library of Science 2023-11-02 /pmc/articles/PMC10621839/ /pubmed/37917652 http://dx.doi.org/10.1371/journal.pone.0293814 Text en © 2023 Li et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Li, Hongxia
Zhang, Yiru
Yang, Wanli
Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis
title Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis
title_full Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis
title_fullStr Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis
title_full_unstemmed Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis
title_short Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis
title_sort gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10621839/
https://www.ncbi.nlm.nih.gov/pubmed/37917652
http://dx.doi.org/10.1371/journal.pone.0293814
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