Title: Machine learning approach to roof fall risks classification in UG mines using Adaboost and XGboost incorporating transfer learning technique

Authors: Jitendra Pramanik; Bijay Kumar Paikaray; Singam Jayanthu; Abhaya Kumar Samal

Addresses: Department of Mining Engineering, National Institute of Technology, Rourkela, Odisha, India ' Center for Data Science, SOA University, Odisha, India ' Department of Mining Engineering, National Institute of Technology, Rourkela, Odisha, India ' Department of CSE, Trident Academy of Technology, Bhubaneswar, Odisha India

Abstract: Roof stability in underground coal mines is critical in commanding mine productivity as well as miners' safety. From this perspective, it is a distinctive challenge to provide a safe working environment along with uncompromised productivity and uninterrupted mining operations. Tested over time, machine learning techniques have evolved as a trusted tool in delivering successful outcomes and in providing trustworthy solutions to many real-life problems in various domains of application that can be safely extended to be adopted in this context. The prime objective of this paper is to propose a transfer learning technique-based approach to classify the occurrence of sudden roof fall based on the available roof sag data. The potency of AdaBoost classification algorithms like decision tree, Gaussian Naïve Bayes, Logistic regression, support vector classifier, and XGBoost classifier based on the roof sag data taken from BG-K2 and BG-K3 panel of GDK-11 incline has been studied and compared.

Keywords: transfer learning techniques; roof fall classification; machine learning techniques; AdaBoost classification; XGBoost.

DOI: 10.1504/IJRIS.2023.136361

International Journal of Reasoning-based Intelligent Systems, 2023 Vol.15 No.3/4, pp.249 - 258

Received: 26 Aug 2022
Accepted: 01 Oct 2022
Published online: 31 Jan 2024 *

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