Areas with vulnerable ecological environments often breed many geological disasters, especially landslides, which pose a severe threat to the safety of people’s lives and property in these areas. To aid in landslide prevention and mitigation, an approach combining the coefficient of determination method (CF) and a deep neural network (DNN) were proposed in this study for landslide susceptibility evaluation. The deep neural network can excavate the deep features of samples and improve the accuracy of the susceptibility model. In addition, the logistic regression model (LRM) and support vector machine (SVM) were selected to create landslide susceptibility maps for comparison, which also involved the coefficient of determination method (CF). Based on landslide remote sensing interpretation and field investigations, a spatial database of mudstone landslides in the Xining area was established. Eight different conditional factors, including the elevation, slope, slope aspect, undulation, curvature, watershed, distance from a fault, and distance from a road, in the study area were selected as the evaluation factors to evaluate the susceptibility. The results revealed that four factors (i.e., the ground elevation, curvature, distance from a fault, and distance from a road) had relatively significant influences on the landslide susceptibility in the study area. Finally, the confusion matrix was used to evaluate the accuracy of the results obtained using the three methods, and the optimal result was selected to evaluate the landslide susceptibility in the study area. It was found that the combined CF-DNN method was more suitable for evaluating the landslide susceptibility in this area. Landslide susceptibility zoning was conducted to divide the study area into four sensitivity levels: low (32.65%), medium (35.12%), high (22.44%), and extremely high (9.79%) susceptibility. The high-risk areas were primarily distributed in the high-elevation areas along the eastern edge of the Huangshui Basin.

The evolution of seepage field in large loess-mudstone landslides is significantly influenced by rainfall infiltration, which is a critical factor affecting their stability. This paper presented a case study of the Hongya Village landslide in Huzhu County, Qinghai Province, to better understand the impact of rainfall infiltration on the triggering mechanism of loess-mudstone landslides. A combination of field investigations, high-density electrical resistivity surveys, and numerical simulations was employed to systematically analyze the temporal and spatial distribution of pore water pressure, volumetric water content, and shear strain within the landslide mass under rainfall infiltration conditions. The results indicated that the rainfall infiltration markedly alters the seepage characteristics of the landslide mass, leading to a sharp increase in pore water pressure and a significant reduction in shear strength, which consequently reduced the overall stability of the landslide. The stability analysis identified a stepwise instability process in the landslide, characterized by “shear displacement-tensile failure-pulling effect” triggered by rainfall infiltration. The findings provide a comprehensive understanding of the instability mechanisms of loess-mudstone landslides under rainfall conditions, thereby offering valuable scientific guidance and technical support for the prevention and mitigation of similar landslide hazards.

Landslides are most severe in the mountainous regions of southwestern China. While landslide identification provides a foundation for disaster prevention operations, methods for utilizing multi-source data and deep learning techniques to improve the efficiency and accuracy of landslide identification in complex environments are still a focus of research and a difficult issue in landslide research. In this study, we address the above problems and construct a landslide identification model based on the shifted window (Swin) transformer. We chose Ya’an, which has a complex terrain and experiences frequent landslides, as the study area. Our model, which fuses features from different remote sensing data sources and introduces a loss function that better learns the boundary information of the target, is compared with the pyramid scene parsing network (PSPNet), the unified perception parsing network (UPerNet), and DeepLab_V3+ models in order to explore the learning potential of the model and test the models’ resilience in an open-source landslide database. The results show that in the Ya’an landslide database, compared with the above benchmark networks (UPerNet, PSPNet, and DeepLab_v3+), the Swin Transformer-based optimization model improves overall accuracies by 1.7%, 2.1%, and 1.5%, respectively; the F1_score is improved by 14.5%, 16.2%, and 12.4%; and the intersection over union (IoU) is improved by 16.9%, 18.5%, and 14.6%, respectively. The performance of the optimized model is excellent.

On 1 September 2022, a landslide in Hongya Village, Weiyuan Town, Huzhu Tu Autonomous County, Qinghai Province, caused significant casualties and economic losses. To mitigate such risks, InSAR technology is employed due to its wide coverage, all-weather operation, and cost-effectiveness in detecting landslides. In this study, focusing on the landslide in Hongya Village, SBAS-InSAR and Sentinel-1A satellite data from July 2021 to September/October 2022 were used to accurately identify the areas of active landslides and to analyze the landslide deformation trends, in combination with the geological characteristics of the landslides and rainfall data. The results showed that strong deformation was detected in the middle and back of the landslide in Hongya Village, with a maximum deformation rate of approximately -13 mm/year. The surface of the landslide consisted of mainly Upper Pleistocene wind-deposited loess, which is extremely sensitive to water. The deformation of the landslide was closely related to the rainfall, and the deformation of the landslide increased with the increase in rainfall. The research results prove that the combination of ascending and descending orbit data based on SBAS-InSAR technology is highly feasible in the field of landslide deformation monitoring and is of great practical significance for landslide disaster prevention and mitigation.

In recent years, various kinds of civil explosive detonation accidents have occurred frequently around the world, resulting in substantial human casualties and significant property losses. It is generally believed that thermal stimulation plays a critical role in triggering the detonation of explosives; consequently, the study of the thermal hazards of explosives is of great significance to many aspects of safety emergency management practices in the production, transportation, storage, and use of explosives. It is known that the thermal stability of the ammonium perchlorate-aluminium system and the ammonium nitrate-aluminium system has been extensively investigated previously in the literature. However, there is a paucity of research on the thermal hazard characteristics of non-ideal explosives under varying oxygen balance conditions within the academic sphere. Therefore, this research focused on the study of the thermal hazards of non-ideal explosives based on thermokinetic analysis. The thermal hazards of non-ideal explosive mixtures of ammonium perchlorate and aluminium and of ammonium nitrate and aluminium were studied by thermal analysis kinetics. The thermokinetic parameters were meticulously studied through differential scanning calorimetry (DSC) analysis. The results showed that the peak reaction temperature and activation energy of the ammonium perchlorate-aluminium system were significantly higher than those of the ammonium nitrate-aluminium system. Under the condition of zero oxygen balance, the peak reaction temperature of the ammonium nitrate-aluminium system was 259 °C (heating rate 5 °C/min), and the activation energy was 84.7 kJ/mol. Under the same conditions, the peak reaction temperature and activation energy of the ammonium perchlorate-aluminium system were 292 °C (heating rate 5 °C/min) and 94.9 kJ/mol, respectively. These results indicate that the ammonium perchlorate-aluminium system has higher safety under the same thermal stimulation conditions. Furthermore, research on both non-ideal explosive systems reveals that the activation energy is at its peak under negative oxygen balance conditions, recorded at 104.2 kJ/mol (ammonium perchlorate-aluminium) and 86.2 kJ/mol (ammonium nitrate-aluminium), which indicates a higher degree of safety. Therefore, the investigation into the thermal hazards of non-ideal explosive systems under different oxygen balance conditions is of utmost importance for the enhancement and improvement of safety emergency management practices.

Qinghai is rich in mineral resources, but frequent and large-scale mineral mining has caused secondary damage to the fragile primary surface and produced a large number of landslide disasters. In complex geological environments such as glacier ablation and frequent tectonic movements, a complete quantitative evaluation method for landslide risk in high-cold mining areas has not yet been formed. In view of this, this article uses the field survey and remote sensing data of the Datong mining area in Qinghai Province in 2012 as the basic data. We comprehensively considered five first-level factors (13 s-level factors) including topography, lithological structure, mining engineering activities, land use, and dynamic deformation as evaluation indicators for landslide susceptibility in mining areas, and used the Topographic Wetness Index (TWI) and the Human Engineering Activity Intensity (HEAI) to quantitatively estimate the hazard of landslide according to the landslide trigger mechanism. The weight-of-evidence approach was used for landslide hazard and risk mapping under different landslide--inducing conditions. The results indicate that the extremely high-hazard areas induced by human engineering activities account for 14% of the total area, and the extremely high-risk areas account for 13% of the total area in the Datong mining area, and the area of the extremely high-risk area is large; the landslide risk assessment mapping model constructed in this study can effectively evaluate the probability of slope instability caused by rainfall and human engineering activities. The effective value of the receiver operating characteristic (ROC) curve of the sensitivity assessment model reaches 0.863, and the evaluation results are consistent with reality; using the weight-of-evidence model for landslide risk assessment is more in line with the actual situation in alpine mining areas, and is more suitable for guiding landslide risk management and disaster prevention and mitigation in mining areas.