Coal mining in mountainous areas is general in China, especially in Shanxi Province. Under the influence of topography in mountainous areas, surface collapses and landslides caused by underground mining happen at a certain frequency and threaten human lives and assets. Accurate prediction of the movement and deformation of mining subsidence in mountainous areas facilitates the prevention and control of geological disasters. The probability integral method is an official prediction method for mining subsidence prediction in China, while it is lacking in the prediction accuracy in mountainous areas due to the inherent topography. Therefore, a practical prediction model based on slopes slip combined parameters optimization was proposed in this study. The slip subsidence and slip horizontal movement were deduced based on the probability integral method considering the topography (slope angle α < 30°) and geological conditions (loess covered) to build the prediction model. The dynamic step fruit fly optimization algorithm (DSFOA) was applied for parameters inversion about the probability integral method in the proposed prediction model, while the other parameters in the proposed model were determined by mechanics analysis based on the nature of losses. The determination of parameters is more efficient, objective and reasonable, so that the prediction accuracy can be improved. The measured data of the working panel 22,101 located in Taiyuan, Shanxi Province was verified by this practical model, and the result shows that the mean square error of subsidence and the horizontal movement was decreased to 71 mm and 276 mm, respectively, hence, the applicability of the proposed mining subsidence prediction model in mountainous areas is verified. This work will contribute to a comprehensive understanding on the law of surface movement and provide theoretical guidance for surface damage prevention and control in mountainous mining areas.

It is imperative to timely and accurately predict the progressive surface subsidence caused by coal mining in the context of precision coal mining. However, the existing dynamic prediction methods that use time functions still have limitations, especially in the description of the moments of initiation and maximum subsidence velocity, which hinder their wide application. In this study, we proposed the MMF (Morgan–Mercer–Flodin) time function for predicting progressive surface subsidence based on the model assumptions and formula derivations. MMF time function can resolve the limitations in the description of the moments of initiation and maximum subsidence velocity perfectly. Afterward, we established the dynamic prediction model by combining the probability integral method with the MMF time function. Finally, using the measured subsidence data of working panel 22101 as an example, the accuracy and reliability of the dynamic prediction model was verified. The average RMSE and average relative RMSE (RRMSE) of prediction progressive subsidence using MMF time function are 46.65 mm and 4.63%, respectively. The accuracy is optimal compared with other time functions (for the average RMSE, Logistic time function is 80.57 mm, Gompertz time function is 79.77 mm, and Weibull time function is 90.61 mm; for the average RRMSE, Logistic time function is 7.66%, Gompertz time function is 7.73%, and Weibull time function is 8.62%). The results show that the method proposed in this paper can fully meet the requirements of practical engineering applications, achieve accurate dynamic prediction during the coal mining process, and provide good guidance for surface deformation and building protection.

The mining of coal resources in eastern China has entered the stage of deep mining, and many mines have reached the depth of 1000 meters. Different from shallow and moderate depth mining, the temporal and spatial evolution regulation of surface movement and deformation under deep mining has its particularity. Combining with the geological and mining conditions of Fengfeng mining area, this paper systematically studies the characteristics of surface movement under the condition of shallow, moderate, and near kilometer mining depth. By means of field measurement, InSAR monitoring, we get the subsidence data under different mining depth and get the relevant subsidence parameters by inversion. Through comparative analysis, the special law of subsidence under the mining depth of 1000 meters is obtained. The results show that under the condition of nearly 1000 meters mining depth, the surface movement and deformation have the characteristics of large displacement angle, small displacement deformation value, and large main influence radius. The regulation of small proportion of active period of maximum subsidence point, gentle shape of surface movement basin, and low mining adequacy are obtained. The research results provide technical references for deep mining under buildings, railways, and water bodies and provide basis and reference for scientific mining and safe recovery of coal pillars in kilometer deep mine.

Mining-induced surface subsidence is a typical geological hazard. Loose layer drainage disturbed by coal mining can exacerbate surface subsidence in terms of both the extent and amount, thereby increasing the risk of building deformation and environmental degradation in mining areas. However, currently the prediction results of surface subsidence considering these two factors are not precise enough, which contradicts the principles of green coal mining. Firstly, this paper introduces the probability integral method, which predicts mining-induced surface subsidence. Subsequently, based on the soil–water coupled theory and the derived characteristic curve of groundwater level decline, a surface subsidence prediction model that considers loose layer drainage is constructed using triple integral transformation. Finally, a more precise surface subsidence prediction model considering both factors is proposed based on the principle of superposition. The model is applied to the mining of working panel 1309 in Shanxi province, China, an area rich in coal yet scarce in water resources. When compared with the measured subsidence data, the proposed model achieves a root mean square error (RMSE) of 27 mm, while the RMSEs of existing models are 78 mm and 123 mm, respectively. The prediction accuracy has been significantly improved. In addition, the proposed model is further validated through fluid–solid coupling numerical calculations in FLAC3D. The subsidence results considering the single effect of each factor also demonstrated good validation accuracy. Overall, the proposed model can accurately describe the surface subsidence considering both factors. This research can provide a theoretical guide for assessing the environmental impact and building damage, while contributing to the sustainable development of land use and groundwater resource in mining areas.