Slope stability analysis is important for the safe mining of mineral resources. The collapse of goafs in loess gullies can lead to natural disasters such as surface landslides. In this context, this study analyzes monitoring data obtained from surface observation in the Shendong mining area of the Hanjiawan coal mine based on the geological conditions therein. The monitoring results show that the working face experiences a starting period, an active period, and a declining period, from the start of mining to the end of the working face. At the initial mining stage, there is no evident surface movement or deformation in the mining area. When the advance distance of the 12106 working face is between 13 m and 109 m, the surface movement and deformation vary significantly, and the maximum subsidence reaches 1963 mm, which is enough to cause landslides. We select the physical and mechanical parameters of the rock and soil in the mine and then simulate the formation mechanism of surface landslides under different slope angles of the mining area using FLAC3D software. Because of the collapse of the mined-out area, the overlying strata structure is destroyed, the subsidence basin is shifted to the center as a whole, and the slope mass is subjected to tensile and compression deformation, resulting in plastic damage, which develops downward along the crack and leads to a collapse because of the discontinuous movement and deformation of the surface; moreover, step-type ground fissures are produced. The results also show that when the slope angle is greater than 60°, the displacement of the slope mass is not uniform, and the rock stratum in a position with large displacement loses its support, leading to landslides; when the slope angle is less than 30°, the bedrock surface forms a sliding surface and develops to the surface, thus decreasing the possibility of landslides. Based on the stability analysis of the collapsed slope in the goaf of the loess gully, a scientific basis is provided for the effective prevention and control of geological disasters in the Shendong mining area.

The accurate prediction of surface subsidence is a significant foundation for the damage assessment of coal seam mining and ecological environment reclamation in loess donga. However, conventional models are very problematic, and the reliability of prediction is usually low. Therefore, we propose a method for predicting surface subsidence of coal seam mining in loess donga that is based on the probability integration model, combined with the movement principle of rock and soil layers in the respective study area, and considering the influence of slope stability and additional mining slip on mining subsidence. The feasibility of our new method was verified by a case study in the N1114 working face of the Ningtiaota coal mine (China) that is situated in an area with abundant loess dongas. The results show that slope slippage is the source of error in the prediction of subsidence in loess donga. The prediction idea of “dividing the surface of loess donga into horizontal strata area and slope sub-area, and predicting the subsidence value of the two areas, respectively” is put forward. A method for predicting the subsidence value of two regions is established. First, based on the theory of probability integral and rock formation movement, the probability integral parameters of the horizontal stratum area are determined, and the subsidence basins in the area are superimposed and calculated. Secondly, according to the slope stability and slip principle, the additional displacement of subsidence in the slope area with mining instability coefficient Gcs > 0.87 is calculated. Finally, combined with the subsidence prediction results of the strata area and the slope sub-area, and the position of the slope, the accurate prediction of the surface subsidence in loess donga is realized. Our results show that the agreement between the curves predicted from our calculations and from the measured data are between 88.7–97.8%. The calculated error of the additional displacement of slope mining slip is between 1.0–9.8%. The excellent correlation between the modelled and measured data documents that our method provides, demonstrated a new efficient and valuable tool for the precise prediction of damages induced by mining of underground coal seams in loess donga.

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