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- W4382119434 abstract "Roof falls are the most hazardous events that occur in underground mines that inflict damage to machinery and loss of human life. Therefore, the use of tools and techniques to ensure the safety of miners as well as machines is of utmost priority. Though there are many factors responsible for the occurrence of such catastrophic events, many of these factors are neither precisely nor accurately measurable, nor are well defined, making the issue of assessment quite uncertain. Machine learning methods have been proved to be a successful tool to provide trustworthy solutions to many mission-critical real-life problems. In this paper, we attempt to explore the extent of applicability of machine learning techniques to adequately predict the occurrences of such catastrophic roof fall events in UG coal mines due to strata control problems. This work has conducted a comparative study of the potency of varied machine learning techniques on the roof convergence data taken GDK-10 underground coal mine." @default.
- W4382119434 created "2023-06-27" @default.
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- W4382119434 date "2023-01-01" @default.
- W4382119434 modified "2023-10-17" @default.
- W4382119434 title "Implementing Machine Learning Algorithms for Predicting Roof Fall Statistics in UG Coal Mines" @default.
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- W4382119434 doi "https://doi.org/10.1007/978-981-99-0412-9_12" @default.
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