Height prediction of water-flowing fracture zone with a genetic-algorithm support-vector-machine method

Abstract

Abstract Prediction of the height of a water-flowing fracture zone (WFFZ) is the foundation for evaluating water bursting conditions on roof coal. By taking the Binchang mining area as the study area and conducting an in-depth study of the influence of coal seam thickness, burial depth, working face length, and roof category on the height of a WFFZ, we proposed that the proportion of hard rock in different roof ranges should be used to characterise the influence of roof category on WFFZ height. Based on data of WFFZ height and its influence index obtained from field observations, a prediction model is established for WFFZ height using a combination of a genetic algorithm and a support-vector machine. The reliability and superiority of the prediction model were verified by a comparative study and an engineering application. The results show that the main factors affecting WFFZ height in the study area are coal seam thickness, burial depth, working face length, and roof category. Compared with multiple-linear-regression and back-propagation neural-network approaches, the height-prediction model of the WFFZ based on a genetic-algorithm support-vector-machine method has higher training and prediction accuracy and is more suitable for WFFZ prediction in the mining area.