A decision tree for rockburst conditions prediction

Detalhes bibliográficos
Autor(a) principal: Owusu-Ansah, Dominic
Data de Publicação: 2023
Outros Autores: Tinoco, Joaquim, Lohrasb, Faramarzi, Martins, Francisco F., Matos, José C.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/85277
Resumo: This paper presents an alternative approach to predict rockburst using Machine Learning (ML) algorithms. The study used the Decision Tree (DT) algorithm and implemented two approaches: (1) using DT model for each rock type (DT-RT), and (2) developing a single DT model (Unique-DT) for all rock types. A dataset containing 210 records was collected. Training and testing were performed on this dataset with 5 input variables, which are: Rock Type, Depth, Brittle Index (BI), Stress Index (SI), and Elastic Energy Index (EEI). Other ML algorithms, such as Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Gradient-Boosting (AdaboostM1), were implemented as a form of comparison to the DT models developed. The evaluation metrics and relative importance were utilized to examine some characteristics of the DT methods. The Unique-DT model showed a promising result of the two DT models, giving an average of (F1 = 0.65) in rockburst condition prediction. Although RF and AdaboostM1 (F1 = 0.66) performed slightly better, Unique-DT is recommended for predicting rockburst conditions because it is easier, more effective, and more accurate.
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spelling A decision tree for rockburst conditions predictionRockburstRockburst conditionDecision treeMachine learning algorithmsPredictionsMetricsEngenharia e Tecnologia::Engenharia CivilThis paper presents an alternative approach to predict rockburst using Machine Learning (ML) algorithms. The study used the Decision Tree (DT) algorithm and implemented two approaches: (1) using DT model for each rock type (DT-RT), and (2) developing a single DT model (Unique-DT) for all rock types. A dataset containing 210 records was collected. Training and testing were performed on this dataset with 5 input variables, which are: Rock Type, Depth, Brittle Index (BI), Stress Index (SI), and Elastic Energy Index (EEI). Other ML algorithms, such as Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Gradient-Boosting (AdaboostM1), were implemented as a form of comparison to the DT models developed. The evaluation metrics and relative importance were utilized to examine some characteristics of the DT methods. The Unique-DT model showed a promising result of the two DT models, giving an average of (F1 = 0.65) in rockburst condition prediction. Although RF and AdaboostM1 (F1 = 0.66) performed slightly better, Unique-DT is recommended for predicting rockburst conditions because it is easier, more effective, and more accurate.FCT -Fundação para a Ciência e a Tecnologia(LA/P/0112/2020)MDPIUniversidade do MinhoOwusu-Ansah, DominicTinoco, JoaquimLohrasb, FaramarziMartins, Francisco F.Matos, José C.2023-05-302023-05-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/85277eng10.3390/app13116655info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-08-12T01:17:49Zoai:repositorium.sdum.uminho.pt:1822/85277Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:42:47.038350Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A decision tree for rockburst conditions prediction
title A decision tree for rockburst conditions prediction
spellingShingle A decision tree for rockburst conditions prediction
Owusu-Ansah, Dominic
Rockburst
Rockburst condition
Decision tree
Machine learning algorithms
Predictions
Metrics
Engenharia e Tecnologia::Engenharia Civil
title_short A decision tree for rockburst conditions prediction
title_full A decision tree for rockburst conditions prediction
title_fullStr A decision tree for rockburst conditions prediction
title_full_unstemmed A decision tree for rockburst conditions prediction
title_sort A decision tree for rockburst conditions prediction
author Owusu-Ansah, Dominic
author_facet Owusu-Ansah, Dominic
Tinoco, Joaquim
Lohrasb, Faramarzi
Martins, Francisco F.
Matos, José C.
author_role author
author2 Tinoco, Joaquim
Lohrasb, Faramarzi
Martins, Francisco F.
Matos, José C.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Owusu-Ansah, Dominic
Tinoco, Joaquim
Lohrasb, Faramarzi
Martins, Francisco F.
Matos, José C.
dc.subject.por.fl_str_mv Rockburst
Rockburst condition
Decision tree
Machine learning algorithms
Predictions
Metrics
Engenharia e Tecnologia::Engenharia Civil
topic Rockburst
Rockburst condition
Decision tree
Machine learning algorithms
Predictions
Metrics
Engenharia e Tecnologia::Engenharia Civil
description This paper presents an alternative approach to predict rockburst using Machine Learning (ML) algorithms. The study used the Decision Tree (DT) algorithm and implemented two approaches: (1) using DT model for each rock type (DT-RT), and (2) developing a single DT model (Unique-DT) for all rock types. A dataset containing 210 records was collected. Training and testing were performed on this dataset with 5 input variables, which are: Rock Type, Depth, Brittle Index (BI), Stress Index (SI), and Elastic Energy Index (EEI). Other ML algorithms, such as Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Gradient-Boosting (AdaboostM1), were implemented as a form of comparison to the DT models developed. The evaluation metrics and relative importance were utilized to examine some characteristics of the DT methods. The Unique-DT model showed a promising result of the two DT models, giving an average of (F1 = 0.65) in rockburst condition prediction. Although RF and AdaboostM1 (F1 = 0.66) performed slightly better, Unique-DT is recommended for predicting rockburst conditions because it is easier, more effective, and more accurate.
publishDate 2023
dc.date.none.fl_str_mv 2023-05-30
2023-05-30T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/85277
url https://hdl.handle.net/1822/85277
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.3390/app13116655
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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