Landslide geological disasters, occurring globally, often result in significant loss of life and extensive economic damage. In recent years, the severity of these disasters has increased, likely due to the frequent occurrence of extreme rainstorms associated with global warming. This escalating trend emphasizes the urgent need for a simple and efficient method to identify hidden dangers related to landslide geological disasters. Areas experiencing seasonal heavy rainfall are particularly susceptible to such disasters, posing a serious threat to the lives and property of local residents. In response to the challenging characteristics of landslide geological hazards, such as their strong concealment and the high vegetation coverage in the Liupan Mountain area of the Loess Plateau, this study focuses on the integrated remote sensing identification and research of hidden landslide dangers in Longde County. The methodology combines differential interferometric synthetic aperture radar technology (D-InSAR) and high-resolution optical remote sensing. Surface deformation information of Longde County was obtained by analyzing 85 Sentinel-1A data from 2019 to mid-2020 using Stacking-InSAR, in conjunction with high-resolution optical remote sensing image data from GF-2 in 2019. Furthermore, the study conducted integrated remote sensing identification and field verification of landslide hazards throughout the entire county. This involved interpreting the shape and deformation marks of landslide hazards, identifying the disaster-bearing bodies, and expertly interpreting the environmental factors contributing to the hazards. As a result, 47 suspected landslide hazards and 21 field investigation points were identified, with 16 hazards verified with an accuracy of 76.19%. This outcome directly confirms the applicability and accuracy of the integrated remote sensing identification technology in the study area. The research results presented in this paper provide an effective scientific and theoretical basis for the monitoring and treatment of landslide geological disasters in the future stages. They also play a pivotal role in the prevention of such disasters.

In this paper, the formation mechanism of a slightly inclined bedding mudstone landslide in the overlying mountain of the coal mining subsidence area of the Tanshan Coal Mine in Ningxia, China, is studied. By means of geotechnical investigation, indoor geotechnical tests, theoretical analysis and other technical means, we find the geological environment background of the study area and obtain the physical and mechanical property indexes of the mining landslide in the Tanshan Coal Mine. By combining the numerical simulation of discrete elements and finite elements, the macro deformation and failure law of the mining mudstone landslide and the displacement and stress nephogram of the failure process are discussed. The results show that the slightly inclined bedding mudstone landslide in the Tanshan Coal Mine is 850 m long from east to west, 500 m wide from north to south and 10,875,000 m³ in volume. It is composed of Jurassic mudstone and is a traction landslide caused by the coal mining subsidence area. The formation of the landslide is affected by internal factors and inducing factors. The internal factors are mainly geotechnical types and engineering geological properties, and the inducing factors are mainly coal mining activities and rainfall. By analyzing and summarizing the calculation process of the slope model prior to the landslide in 2D-Block and GeoStudio numerical simulation software, the sliding process of the slightly inclined bedding mudstone landslide in the Tanshan Coal Mine is divided into four stages: slope creep, slope deformation, landslide movement and landslide accumulation. GeoStudio software is used to calculate the stability of the Tanshan Coal Mine landslide under natural and rainfall conditions. The landslide is in a stable state under natural conditions and is basically stable under rainfall conditions. By comparing the calculation results of the limit equilibrium method and the finite element limit equilibrium method, we find that the calculated stability coefficient is more accurate when the appropriate constitutive model is selected. The research results have important reference significance for the prevention and control of the gently inclined bedding mudstone landslide of the overlying mountain in the coal mining subsidence area of the Loess Plateau.

The escalating consequences of human activities and global warming have markedly increased the frequency and intensity of geological disasters worldwide, posing a formidable threat to human life and property. In the southern mountainous region of Ningxia, China—an area characterized by complex topography, interlaced ravines, and pronounced ecological fragility—recurrent geological disasters have substantially constrained rural revitalization and development. This study introduces the integration of the Information Value (IV) method with Random Forest (RF) and XGBoost models, identifying IV + XGBoost as the optimal model through rigorous ROC-curve validation. The results reveal that low- and lower-risk areas account for 58.63% of the total area (7644.20 km² and 4038.08 km²), medium-risk areas cover 29.24% (5825.76 km²), and high-risk regions constitute 12.13% (2417.28 km²). The latter are predominantly in river valleys with high population density and intensive economic activities. These findings provide practical recommendations for scientifically informed disaster management and decision-making by relevant authorities. Furthermore, the proposed methodology offers valuable insights for disaster risk assessment in other regions with similar complex terrains and ecological vulnerabilities, contributing to developing more effective and sustainable disaster mitigation strategies.