Mine water inrush source identification model based on PCA-GA-RF

Abstract

Mine sudden water has become one of the main hazards affecting the safety production of mines, and rapid and accurate identification of the type of sudden water source is a key step in the management of mine sudden water disaster, so a PCA-GA-RF-based mine sudden water source identification model is proposed. Based on the measured data of 88 groups of water samples from Xieqiao Coal Mine in Yingshang County, Anhui Province, and following the principle of stratified random sampling, it was divided into 62 groups of training samples and 26 groups of prediction samples according to the ratio of 7:3, and the four principal components were extracted by PCA to construct the PCA-GA-RF model, and compare it with the PCA-RF, PCA-ABC-RF and PCA-FA-RF models. The results show that the PCA-GA-RF model discriminates the results with an accuracy of 96.153 8%, which is superior with the highest accuracy, precision, recall and F1 value compared with other models.

Keywords

• mine water inrush
• water source identification
• principal component analysis (pca)
• random forest(rf)
• genetic algorithm(ga)