Research on application of PCA-WNN model in predicting the development height of water-flowing fractured zones

The water-flowing fractured zone serves as the primary pathway for roof water influx in coal mines.Accurate prediction of the development height of this zone is crucial for anticipating and mitigating roof water hazards.Given the intricacies of the water-flowing fractured zone and the interdependencies among predictive factors,we have combined practical coal production data with engineering geological theory. Five key factors were identified: mining height,inclined length of the working face,ratio coefficient of hard rock lithology,mining depth,and the coal seam dip angle.By combining Principal Component Analysis（PCA）and Wavelet Neural Network（WNN）,correlations and redundant information among the main controlling factors were eliminated through PCA.The uncorrelated principal components were subsequently used as input factors for WNN to establish the PCA-WNN model for predicting the height of the water-flowing fractured zone.The results indicated that the PCA-WNN model effectively eliminated correlations among