Machine learning approach to roof fall risks classification in UG mines using Adaboost and XGboost incorporating transfer learning technique
by Jitendra Pramanik; Bijay Kumar Paikaray; Singam Jayanthu; Abhaya Kumar Samal
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 15, No. 3/4, 2023

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.

Online publication date: Wed, 31-Jan-2024

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