Open AccessReal-Time Prediction Model of Coal and Gas Outburst
Author(s) -
Yandong Ru,
Xingfeng Lv,
Jifeng Guo,
Hongquan Zhang,
Lijuan Chen
Publication year - 2020
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2020/2432806
Subject(s) - coal , missing data , data mining , value (mathematics) , correlation coefficient , identification (biology) , predictive modelling , computer science , statistics , engineering , mathematics , waste management , botany , biology
Coal and gas outburst has been one of the main threats to coal mine safety. Accurate coal and gas outburst prediction is the key to avoid accidents. The data is actual and complete by default in the existing prediction model. However, in fact, data missing and abnormal data value often occur, which results in poor prediction performance. Therefore, this paper proposes to use the correlation coefficient to complete the missing data filling in real time for the first time. The abnormal data identification is completed based on the Pauta criterion. Random forest model is used to realize the prediction model. The prediction performance of sensitivity 100%, accuracy 97.5%, and specificity 84.6% were obtained. Experiments show that the model can complete the prediction of coal and gas outburst in real time under the condition of missing data and abnormal data value, which can be used as a new prediction model of coal and gas outburst.