Study on Height Prediction of Water Flowing Fractured Zone in Deep Mines Based on Weka Platform

Abstract

Accurately predicting the height of water flowing fractured zone is of great significance to coal mine safety mining. In recent years, most mines in China have entered deep mining. Aiming at the problem that it is difficult to accurately predict the height of water flowing fractured zone under the condition of large mining depth, the mining depth, height mining, inclined length of working face and coefficient of hard rock lithology ratio are selected as the main influencing factors of the height of water flowing fractured zone. The relationship between various factors and the height of water flowing fractured zone is analyzed by SPSS software. Based on the data mining tool Weka platform, Bayesian classifier, artificial neural network and support vector machine model are used to mine and analyze the measured data of water flowing fractured zone, and the detailed accuracy, confusion matrix and node error rate are compared. The results show that, the accuracy rate of instance classification of the three models is greater than 60%. The accuracy of the artificial neural network model is the highest and the node error rate is the lowest. In general, the training effect of the artificial neural network model is the best. By predicting engineering examples, the prediction accuracy of the model reaches 80%, and a good prediction effect is obtained. The height prediction system of water flowing fractured zone is developed based on VB language, which can provide a reference for the prediction of the height failure grade of water flowing fractured zone.