Application of MI and SVM in coal and gas outburst prediction

doi:10.16265/j.cnki.issn 1003-3033.2021.01.011

Abstract

Abstract: In order to address problems of small quantity of accidents training data and lack of data set that can be used in coal and gas outburst prediction model, MI data mining algorithm was presented to fill up missing parameters in accident data and increase available data sets. Then, imputed data were employed in SVM prediction model's training and testing, and K-Nearest Neighbors (KNN) algorithm was selected and compared with SVM. The results show that the average recognition rate of SVM algorithm is 88.37% and 88.87% before and after data inputting respectively, and recognition rate of accident data is 79.71% and 91.27% respectively. That of KNN algorithm before and after data inputting is 87.59% and 88.37% respectively while accident recognition rate being 70.4% and 84.23%, respectively. Therefore, MI has little effect on improving average recognition rate, but has a greater one on improving accident recognition rate, which can improve prediction rate of coal and gas outburst accidents. The incident recognition rate of SVM algorithm is higher than that of KNN algorithm after data inputting.

Key words: multiple imputation (MI), support vector machine (SVM), coal and gas outburst, prediction, accident recognition rate

CLC Number:

X936

ZHENG Xiaoliang, LAI Wenhao, XUE Sheng. Application of MI and SVM in coal and gas outburst prediction[J]. China Safety Science Journal, 2021, 31(1): 75-80.

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