Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/85277
Título: | A decision tree for rockburst conditions prediction |
Autor(es): | Owusu-Ansah, Dominic Tinoco, Joaquim Lohrasb, Faramarzi Martins, Francisco F. Matos, José C. |
Palavras-chave: | Rockburst Rockburst condition Decision tree Machine learning algorithms Predictions Metrics |
Data: | 30-Mai-2023 |
Editora: | MDPI |
Revista: | Applied Sciences |
Resumo(s): | 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. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/85277 |
DOI: | 10.3390/app13116655 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | ISISE - Artigos em Revistas Internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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A Decision Tree for Rockburst Conditions Prediction_(Dominic2023).pdf | Journal paper | 2,46 MB | Adobe PDF | Ver/Abrir |