Using random forest for the risk assessment of coal-floor water inrush in Panjiayao Coal Mine, northern China | Utilisation d’une forêt aléatoire pour l’évaluation des risques liés à l’irruption de l’eau dans le charbon de la mine de charbon de Panjiayao, dans le nord de la Chine Usando Bosque Aleatorio para la evaluación de riesgo de afluencias de agua en mina Panjiayao, una mina de carbón en el Norte de China 基于随机森林的中国北方潘家窑煤矿煤层底板突水危险性评价 Uso de floresta aleatória para a avaliação do risco de inrush da água do piso de carvão na mina de carvão de Panjiayao, norte da China
2018
Zhao, Dekang | Wu, Qiang | Cui, Fangpeng | Xu, Hua | Zeng, Yifan | Cao, Yufei | Du, Yuanze
Coal-floor water-inrush incidents account for a large proportion of coal mine disasters in northern China, and accurate risk assessment is crucial for safe coal production. A novel and promising assessment model for water inrush is proposed based on random forest (RF), which is a powerful intelligent machine-learning algorithm. RF has considerable advantages, including high classification accuracy and the capability to evaluate the importance of variables; in particularly, it is robust in dealing with the complicated and non-linear problems inherent in risk assessment. In this study, the proposed model is applied to Panjiayao Coal Mine, northern China. Eight factors were selected as evaluation indices according to systematic analysis of the geological conditions and a field survey of the study area. Risk assessment maps were generated based on RF, and the probabilistic neural network (PNN) model was also used for risk assessment as a comparison. The results demonstrate that the two methods are consistent in the risk assessment of water inrush at the mine, and RF shows a better performance compared to PNN with an overall accuracy higher by 6.67%. It is concluded that RF is more practicable to assess the water-inrush risk than PNN. The presented method will be helpful in avoiding water inrush and also can be extended to various engineering applications.
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