@article{ref1,
title="A prediction and early warning model of miners' unsafe behavior based on random forest",
journal="China safety science journal (CSSJ)",
year="2022",
author="Li, H. and Xu, H. and Tian, S.",
volume="32",
number="12",
pages="10-18",
abstract="In order to reduce the unsafe behaviors of coal miners and make scientific judgement of current status and future trend, this study constructed a database of accidents of coal mines and an attribute table of unsafe behaviors of miners with accident statistics and literatures. On this basis, association rules between unsafe factors and behaviors were explored by adopting Apriori algorithm. An original model was established to fit training, which was formed by taking strong correlation rule as input indicators to the random forest prediction model and the frequency of unsafe behaviors as output indicators. Early warning threshold based on correlation analysis and expert recommendations was also set to refine the early warning model of miners' unsafe behaviors. The results show that safety culture, on-site monitoring, ineffective correction, technical environment, and personnel status constitute strong correlation rules with miners' unsafe behaviors. For the prediction of miners' unsafe behavior, the predicted value has less deviations from the actual, and the early warning model has relatively high accuracy. Among them, technical environment, work environment, organizational management, and identification of hazards play the key role in predicting outcomes. © 2022 China Safety Science Journal. All rights reserved.<p /><p>Language: zh</p>",
language="zh",
issn="1003-3033",
doi="10.16265/j.cnki.issn1003-3033.2022.12.2752",
url="http://dx.doi.org/10.16265/j.cnki.issn1003-3033.2022.12.2752"
}