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

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