A Prediction Method for Height of Water Flowing Fractured Zone Based on Sparrow Search Algorithm–Elman Neural Network in Northwest Mining Area

Abstract

The main Jurassic coal seams of the Ordos Basin of northwest mining area have special hosting conditions and complex hydrogeological conditions, and the high-intensity coal mining of the coal seams is likely to cause groundwater loss and negative effects on the surface ecological environment. The research was aimed at predicting the height of the water-flowing fractured zone (WFFZ) in high-intensity coal mining in that area and gave instructions for avoiding water inrush accidents and realizing damage reduction mining during the actual mining procedure of the coal mine. In this study, 18 samples of the measured height of WFFZ in Jurassic coal seams were systematically collected. In the mining method, the ratio of the thickness of the hard rock to the thickness of the soft rock in the bedrock, buried depth, mining height, and working face length was selected as the input vectors, applied the sparrow search algorithm (SSA) to iteratively optimize the weights and thresholds of the Elman neural network (ENN), constructed an SSA-Elman neural network model. The results demonstrate that the improved SSA-Elman neural network model has higher accuracy in predicting the height of the WFFZ compared with traditional prediction algorithms. The results of this study help guide damage-reducing, water-preserving mining of the middle-deep buried Jurassic coal seams in the northwest mining areas.