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Comparative study of multiple machine learning algorithms for risk level prediction in goaf
With the acceleration of the mining process, the goaf has become one of the main sources of danger in underground mines, seriously threatening the safe production of mines. To make an accurate prediction of the risk level of the goaf quickly, this paper optimizes the features of the goaf by correlat...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10448475/ https://www.ncbi.nlm.nih.gov/pubmed/37636440 http://dx.doi.org/10.1016/j.heliyon.2023.e19092 |
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author | Zhang, Bin Hu, Shaohua Li, Moxiao |
author_facet | Zhang, Bin Hu, Shaohua Li, Moxiao |
author_sort | Zhang, Bin |
collection | PubMed |
description | With the acceleration of the mining process, the goaf has become one of the main sources of danger in underground mines, seriously threatening the safe production of mines. To make an accurate prediction of the risk level of the goaf quickly, this paper optimizes the features of the goaf by correlation analysis and feature importance and constructs a combination of feature parameters for the risk level prediction of the goaf to solve the problem of redundancy of evaluation indexes. Multiple machine learning algorithms are applied to 121 sets of goaf data respectively, and the optimal algorithm and the best combination of feature parameters are obtained by evaluating the mining area with multiple indicators such as accuracy and kappa coefficient. The best combination of features parameters are ground-water, goaf layout, volume of goaf, goaf volume, span-height ratio, and mining disturbance, and the optimal algorithm is Extra Tree (ET), which needles the goaf risk level prediction problem with the accuracy of 94%. This model can be used to solve the problem of how to quickly and accurately predict the risk level of the goaf. |
format | Online Article Text |
id | pubmed-10448475 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-104484752023-08-25 Comparative study of multiple machine learning algorithms for risk level prediction in goaf Zhang, Bin Hu, Shaohua Li, Moxiao Heliyon Research Article With the acceleration of the mining process, the goaf has become one of the main sources of danger in underground mines, seriously threatening the safe production of mines. To make an accurate prediction of the risk level of the goaf quickly, this paper optimizes the features of the goaf by correlation analysis and feature importance and constructs a combination of feature parameters for the risk level prediction of the goaf to solve the problem of redundancy of evaluation indexes. Multiple machine learning algorithms are applied to 121 sets of goaf data respectively, and the optimal algorithm and the best combination of feature parameters are obtained by evaluating the mining area with multiple indicators such as accuracy and kappa coefficient. The best combination of features parameters are ground-water, goaf layout, volume of goaf, goaf volume, span-height ratio, and mining disturbance, and the optimal algorithm is Extra Tree (ET), which needles the goaf risk level prediction problem with the accuracy of 94%. This model can be used to solve the problem of how to quickly and accurately predict the risk level of the goaf. Elsevier 2023-08-11 /pmc/articles/PMC10448475/ /pubmed/37636440 http://dx.doi.org/10.1016/j.heliyon.2023.e19092 Text en © 2023 The Authors. Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Zhang, Bin Hu, Shaohua Li, Moxiao Comparative study of multiple machine learning algorithms for risk level prediction in goaf |
title | Comparative study of multiple machine learning algorithms for risk level prediction in goaf |
title_full | Comparative study of multiple machine learning algorithms for risk level prediction in goaf |
title_fullStr | Comparative study of multiple machine learning algorithms for risk level prediction in goaf |
title_full_unstemmed | Comparative study of multiple machine learning algorithms for risk level prediction in goaf |
title_short | Comparative study of multiple machine learning algorithms for risk level prediction in goaf |
title_sort | comparative study of multiple machine learning algorithms for risk level prediction in goaf |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10448475/ https://www.ncbi.nlm.nih.gov/pubmed/37636440 http://dx.doi.org/10.1016/j.heliyon.2023.e19092 |
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