Prediction method of coal mine gas occurrence law based on multi-source data fusion

Abstract

To improve the prediction accuracy and time-consuming of coal mine gas occurrence law (OL), a new prediction method based on multi-source data fusion is proposed in this paper. Firstly, the method obtains the data of coal mine gas OL, determines the key data required in prediction through decision matrix, and preprocesses the data to reduce the influence of regular noise data. This paper analyzes the basic principle of multi-source data fusion, constructs the prediction model of coal mine gas OL with this technology, takes the optimal value of weighting factor as the input value of the model, and completes the design of coal mine gas OL prediction method based on multi-source data fusion. The experimental results show that the accuracy of this method can reach 98%, while that of the other two traditional methods is lower than the existing methods. This method has high accuracy and efficiency in predicting the coal mine gas OL.