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Abstract Based on the mining conditions of shallow close coal seams in the Jurassic coalfield of northern Shaanxi, in order to explore the evolution and development height prediction of water-conducting fracture zone (WCFZ), combining with measured statistical analysis, physical simulation, numerical calculation, and theoretical analysis, the evolution of mining-induced fractures and the development characteristics of WCFZ are analyzed, and the development height predicting method of WCFZ in shallow close coal seams mining is proposed. The results show that in shallow single coal seam mining, during the stage of incritical mining, the height of the WCFZ increases in a “jumping” pattern with the main roof periodic caving. After critical mining, it is basically stable. Its development height is positively correlated with the mining height. The ratio of development height to mining height is generally 20–30, and the change is not significant with the mining height, overall, the distribution of the WCFZ is “saddle shaped”. Based on close distance coal seams repeated mining, the primary fractures act as activation development when the interburden broken completely, the development of WCFZ at the mining boundary is significantly intensified, and ratio of the height to mining height in repeated mining is obviously lower than that in single seam mining, which is generally 18–25. The traditional empirical formula cannot be applied to the development prediction in shallow close coal seams mining, with an error of 10–50%. Considering the mining height, interburden thickness, and roof caving characteristics, the development height predicting method of WCFZ in shallow close coal seams mining is proposed. Compared with measured results, the calculation error is basically within 15%, which has good prediction accuracy, The research results can provide a theoretical basis for predicting the height of WCFZ in shallow close coal seams mining in the Jurassic coalfield of northern Shaanxi.

The development height of the water-conducting fracture zone (WCFZ) is crucial for the safe production of coal mines. The back-propagation neural network (BP-NN) can be utilized to forecast the WCFZ height, aiding coal mines in water hazard prevention and control efforts. However, the stochastic generation of initial weights and thresholds in BP-NN usually leads to local optima, which might reduce the prediction accuracy. This study thus invokes the excellent global optimization capability of the Improved Radial Movement Optimization (IRMO) algorithm to optimize BP-NN. The influences of mining thickness, coal seam depth, working width, and hard rock lithology proportion coefficient on the height of WCFZ are investigated through 75 groups of in situ data of WCFZ heights measured in the Jurassic coalfield of the Ordos Basin. Consequently, an IRMO-BP-NN model for predicting WCFZ height in the Jurassic coalfield of the Ordos Basin was constructed. The proposed IRMO-BP-NN model was validated through monitoring data from the 4−2216 working faces of Jianbei Coal Mine, followed by a comparative analysis with empirical formulas and conventional BP-NN models. The relative error of the IRMO-BP-NN prediction model is 4.93%, outperforming both the BP-NN prediction model, the SVR prediction model, and empirical formulas. The results demonstrate that the IRMO-BP-NN model enhances the accuracy of predicting WCFZ height, providing an application foundation for predicting such heights in the Jurassic coalfield of the Ordos Basin and protecting the ecological environment of Ordos Basin mining areas.