Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation

Back-break is an adverse event in blasting works that causes the instability of mine walls, equipment collapsing, and reduction in effectiveness of drilling. Therefore, it boosts the total cost of mining operations. This investigation intends to develop optimized support vector machine models to for...

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Autores principales:	Qun Yu, Masoud Monjezi, Ahmed Salih Mohammed, Hesam Dehghani, Danial Jahed Armaghani, Dmitrii Vladimirovich Ulrikh
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	blasting back-break SVM metaheuristic algorithms moth–flame optimization whale optimization algorithm Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/addc2e189bd64ea0b6dac80c2d80fd60
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spelling	oai:doaj.org-article:addc2e189bd64ea0b6dac80c2d80fd602021-11-25T19:04:24ZOptimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation10.3390/su1322127972071-1050https://doaj.org/article/addc2e189bd64ea0b6dac80c2d80fd602021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12797https://doaj.org/toc/2071-1050Back-break is an adverse event in blasting works that causes the instability of mine walls, equipment collapsing, and reduction in effectiveness of drilling. Therefore, it boosts the total cost of mining operations. This investigation intends to develop optimized support vector machine models to forecast back-break caused by blasting. The Support Vector Machine (SVM) model was optimized using two advanced metaheuristic algorithms, including whale optimization algorithm (WOA) and moth–flame optimization (MFO). Before the models’ development, an evolutionary random forest (ERF) technique was used for input selection. This model selected five inputs out of 10 candidate inputs to be used to predict the back break. These two optimized SVM models were evaluated using various performance criteria. The performance of these two models was also compared with other hybridized SVM models. In addition, a sensitivity evaluation was made to find how the selected inputs influence the back-break magnitude. The outcomes of this study demonstrated that both the SVM–MFO and SVM–WOA improved the performance of the standard SVM. Additionally, the SVM–MFO showed a better performance than the SVM–WOA and other hybridized SVM models. The outcomes of this research recommend that the SVM–MFO can be considered as a powerful model to forecast the back-break induced by blasting.Qun YuMasoud MonjeziAhmed Salih MohammedHesam DehghaniDanial Jahed ArmaghaniDmitrii Vladimirovich UlrikhMDPI AGarticleblastingback-breakSVMmetaheuristic algorithmsmoth–flame optimizationwhale optimization algorithmEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12797, p 12797 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	blasting back-break SVM metaheuristic algorithms moth–flame optimization whale optimization algorithm Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	blasting back-break SVM metaheuristic algorithms moth–flame optimization whale optimization algorithm Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Qun Yu Masoud Monjezi Ahmed Salih Mohammed Hesam Dehghani Danial Jahed Armaghani Dmitrii Vladimirovich Ulrikh Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation
description	Back-break is an adverse event in blasting works that causes the instability of mine walls, equipment collapsing, and reduction in effectiveness of drilling. Therefore, it boosts the total cost of mining operations. This investigation intends to develop optimized support vector machine models to forecast back-break caused by blasting. The Support Vector Machine (SVM) model was optimized using two advanced metaheuristic algorithms, including whale optimization algorithm (WOA) and moth–flame optimization (MFO). Before the models’ development, an evolutionary random forest (ERF) technique was used for input selection. This model selected five inputs out of 10 candidate inputs to be used to predict the back break. These two optimized SVM models were evaluated using various performance criteria. The performance of these two models was also compared with other hybridized SVM models. In addition, a sensitivity evaluation was made to find how the selected inputs influence the back-break magnitude. The outcomes of this study demonstrated that both the SVM–MFO and SVM–WOA improved the performance of the standard SVM. Additionally, the SVM–MFO showed a better performance than the SVM–WOA and other hybridized SVM models. The outcomes of this research recommend that the SVM–MFO can be considered as a powerful model to forecast the back-break induced by blasting.
format	article
author	Qun Yu Masoud Monjezi Ahmed Salih Mohammed Hesam Dehghani Danial Jahed Armaghani Dmitrii Vladimirovich Ulrikh
author_facet	Qun Yu Masoud Monjezi Ahmed Salih Mohammed Hesam Dehghani Danial Jahed Armaghani Dmitrii Vladimirovich Ulrikh
author_sort	Qun Yu
title	Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation
title_short	Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation
title_full	Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation
title_fullStr	Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation
title_full_unstemmed	Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation
title_sort	optimized support vector machines combined with evolutionary random forest for prediction of back-break caused by blasting operation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/addc2e189bd64ea0b6dac80c2d80fd60
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Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation

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