Abstract
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.
Highlights
China is one of the countries with serious coal and gas outburst disasters which pose a great threat to the coal mine safety production [1]. e accidents caused by coal and gas outburst account for 38% of safety accidents in coal mine [2], which is the most dangerous and frequent accident type in coal mine accidents
E common prediction methods of coal and gas outburst include the index prediction method and mathematical model prediction method [3]. e index prediction method is to detect the values of various indexes and compare them with the standard values of indexes to determine whether they are dangerous for coal and gas outburst
The amount of data will become very large, so it will take a lot of time to realize data filling so as to affect the prediction speed [7]. erefore, this paper proposes a numerical filling data method based on the correlation of variables which can be considered as real time
Summary
China is one of the countries with serious coal and gas outburst disasters which pose a great threat to the coal mine safety production [1]. e accidents caused by coal and gas outburst account for 38% of safety accidents in coal mine [2], which is the most dangerous and frequent accident type in coal mine accidents. E common prediction methods of coal and gas outburst include the index prediction method and mathematical model prediction method [3]. The existing prediction methods are based on the ideal data to complete the prediction of coal and gas outburst. When the new abnormal data is coming, there is a lack of processing capacity resulting in the inability to complete the prediction in real time. For this reason, this paper proposes a coal and gas outburst prediction model. According to the characteristics of data from the coal mine, the data processing method is given, which can accurately predict coal and gas outburst in practical application. According to the characteristics of data from the coal mine, the data processing method is given, which can accurately predict coal and gas outburst in practical application. e prediction of coal and gas outburst ensures the safety of miner and property of miners
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