Neural network–based prediction methods for height of water-flowing fractured zone caused by underground coal mining

Abstract

The accurate calculation of the height of water-flowing fractured zone (WFFZ) in coal mine is a critical factor in ensuring mine safety and protecting surficial eco-environment. In view of inapplicability of traditional empirical formula for predicting the height of WFFZ, the correlation of the height of WFFZ and influence factors was analyzed firstly based on 82 collected groups of coalfield measured data in China. Results show that the mining thickness and mining depth have a significant effect on the height of WFFZ. Subsequently, the measured data were divided into two parts: 80% for training models and the remaining 20% for validation. Two prediction models, i.e., the multiple regression (MR) model and BP neural network (BPNN) model, were established and trained. A new merging model, multiple regression-BP neural network (MR-BPNN) model, was proposed by combining the multiple regression and BP neural network model. The prediction accuracy and generalization ability of the three models were verified by the recorded testing samples. Results of comparison suggest that three models all had better applicability for predicting the height of WFFZ in the coalmine, compared with the existing empirical prediction methods. More importantly, the MR-BPNN merging model combined the nonlinear mapping ability of neural network and empiric of multiple regression model, which could provide high-accurate, strong-generalized, and practical application for predicting the height of WFFZ of coalfield. In addition, the reason for the inapplicability of traditional empirical formula and the practicability of the proposed neural network-based prediction models were discussed.