首页|New Findings from University of Arizona Describe Advances in Machine Learning (A dvancements In Machine Learning Techniques for Coal and Gas Outburst Prediction In Underground Mines)
New Findings from University of Arizona Describe Advances in Machine Learning (A dvancements In Machine Learning Techniques for Coal and Gas Outburst Prediction In Underground Mines)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject o f a report. According to news reporting originating from Tucson, Arizona, by New sRx correspondents, research stated, “Coal and gas outbursts are a major cause o f fatalities in underground coal mines and pose a threat to coal power generatio n worldwide. Among the current mitigation efforts include monitoring methane gas levels using sen-sors, employing geophysical surveys to identify geological str uctures and zones prone to outbursts, and using empirical modeling for outburst predictions.” Our news editors obtained a quote from the research from the University of Arizo na, “However, in the wake of industry 4.0 technologies, several studies have bee n conducted on applying artificial intelligence methods to predict outbursts. Th e proposed models and their results vary significantly in the literature. This s tudy reviews the application of machine learning (ML) to predict coal and gas ou tbursts in underground mines using a mixed-method approach. Most of the availabl e literature, with a focus on ML applications in coal and gas outburst predictio n, was investigated in China. Findings indicate that researchers proposed divers e ML models mostly combined with different optimization algorithms, including pa rticle swarm optimization (PSO), genetic algorithm (GA), rough set (RS), and fru it fly optimization algorithm (IFOA) for outburst prediction. The number and typ e of input parameters used for prediction differed significantly, with initial g as velocity being the most dominant parameter for gas outbursts, and coal seam d epth as the dominant parameter for coal outbursts. The datasets for training and testing the proposed ML models in the literature varied significantly but were mostly insufficient - which questions the reliability of some of the applied ML models.”
Tucson, Arizona, United States, North an d Central America, Cyborgs, Emerging Technologies, Machine Learning, Mining and Minerals, University of Arizona
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