The selection of evaluation units in geological hazard evaluation systems is crucial for the evaluation results. In an evaluation system, relevant geological evaluation factors are selected and the study area is divided into multiple regular or irregular independent units, such as grids, slopes, and basins. Each evaluation unit, which includes evaluation factor attributes and hazard point distribution data, is placed as an independent individual in a corresponding evaluation model for use in a calculation, and finally a risk index for the entire study area is obtained. In order to compare the influence of the selection of grid units or slope units—two units frequently used in geological hazard evaluation studies—on the accuracy of evaluation results, this paper takes Yuanyang County, Yunnan Province, China, as a case study area. The area was divided into 7851 slope units by the catchment basin method and 12,985,257 grid units by means of an optimal grid unit algorithm. Nine evaluation factors for geological hazards were selected, including elevation, slope, aspect, curvature, land-use type, distance from a fault, distance from a river, engineering geological rock group, and landform type. In order to ensure the objective comparison of evaluation results for geological hazard susceptibility with respect to grid units and slope units, the weighted information model combining the subjective weighting AHP (analytic hierarchy process) and the objective statistical ICM (information content model) were used to evaluate susceptibility with both units. Geological risk evaluation results for collapses and landslides under heavy rain (25–50 mm), rainstorm (50–100 mm), heavy rainstorm (150–250 mm), and extraordinary rainstorm (>250 mm) conditions were obtained. The results showed that the zoning results produced under the slope unit system were better than those produced under the grid unit system in terms of the distribution relationship between hazard points and hazard levels. In addition, ROC (receiver operating characteristic) curves were used to test the results of susceptibility and risk assessments. The AUC (area under the curve) values of the slope unit system were higher than those of the grid unit system. Finally, the evaluation results obtained with slope units were more reasonable and accurate. Compared with the results from an actual geological hazard susceptibility and risk survey, the evaluation results for collapse and landslide geological hazards under the slope unit system were highly consistent with the actual survey results.

Geological hazards, particularly in mountainous regions, represent significant threats to life, property, and the environment. In this study, we focus on Luoping County, Yunnan Province, China, employing SBAS-InSAR technology to monitor surface deformation between 8 October 2022 and 27 September 2024. By integrating InSAR deformation data with 10 static disaster-causing factors, including elevation, slope, aspect, curvature, distance to faults, distance to rivers, distance to roads, engineering geological rock groups, geomorphological types, and the NDVI, geological hazard susceptibility was assessed using the information value (IV) model and the information value–random forest (IV-RF) coupled model. Accuracy validation using ROC curves indicated that the IV-RF model, integrated with InSAR deformation data, achieved the highest accuracy, with an AUC value of 0.805. Based on the susceptibility evaluation, rainfall intensity was introduced as a triggering factor to assess geological hazard risks under four rainfall conditions: 10-year, 20-year, 50-year, and 100-year return periods. The results demonstrated that incorporating InSAR deformation data significantly improved disaster prediction accuracy, providing more reliable and sustainable risk assessment outcomes. This study underscores the critical role of InSAR technology, combined with rainfall conditions, in enhancing the precision of geological hazard risk assessments, offering a scientific basis for disaster prevention and mitigation strategies in Luoping County and similar regions.

The nine plateau lake watersheds in Yunnan are important ecological security barriers in the southwest of China. The prevention and control of landslides are important considerations in the management of these watersheds. Taking the Dianchi Lake watershed as a typical research area, a comprehensive modeling and assessment process of landslide susceptibility was put forward. The comprehensive process was based on the weight of evidence (WoE) method, and many statistical techniques were integrated, such as cross-validation, multi-quantile cumulative Student’s comprehensive weight statistics, independence testing, step-by-step modeling, ROC analysis, and ROC-based susceptibility zoning. In this paper, fourteen models with high accuracy and validity were established, and the AUC reached 0.83–0.87 and 0.85–0.88, respectively. In addition, according to the susceptibility zoning map compiled via the optimal model, 80% of landslides can be predicted in the very-high- and high-susceptibility areas, which only account for 19.58% of the study area. Finally, this paper puts forward strategies for geological disaster prevention and ecological restoration deployment.

The nine plateau lake watersheds in Yunnan are important ecological security barriers for southwest China, and the prevention and control of landslides are important considerations in the management of these watersheds. Taking Dianchi Lake watershed as a typical study area, this paper puts forward an improved comprehensive process of landslide susceptibility evalua-tion, and discusses the model’s sensitivity regarding susceptibility to landslides. The compre-hensive process is based on the weight of evidence method (WoE) and integrates many analyti-cal techniques, such as cross-validation, multi-quantile cumulative Student’s comprehensive weight analysis, independence tests, step-by-step modeling, ROC analysis and ROC-based sus-ceptibility zoning. This paper established fourteen models, with high accuracy and validity AUC, reaching 0.83-0.87 and 0.85-0.88, respectively. Moreover, according to the susceptibility zoning map compiled using the optimal model, 80% of landslides can be predicted in the very high and high susceptibility zones, which only account for 19.58% of the study area. Finally, the strategies for geological disaster prevention and ecological restoration deployment were put forward, providing great guiding significance for deeply understanding the landslide susceptibility of lake watersheds (Dianchi Lake) in Yunnan Plateau and guiding the planning and deployment of landslide prevention and mitigation activities and ecological restoration in the watershed.

Landslide risk management contributes to the sustainable development of the region. Understanding the spatial controls on the distribution of landslides triggered by earthquakes (EqTLs) is difficult in terms of the prediction and risk assessment of EqTLs. In this study, landslides are regarded as a spatial point pattern to test the controls on the spatial distribution of landslides and model the landslide density prediction. Taking more than 190,000 landslides triggered by the 2008 Wenchuan Ms 8.0 earthquake (WcEqTLs) as the research object, the relative density estimation, Kolmogorov–Smirnov testing based on cumulative distribution, receiver operating characteristic curve (ROC) analysis, and Poisson density modeling are comprehensively applied to quantitatively determine and discuss the different control effects of seven factors representing earthquakes, geology, and topography. The distance to the surface ruptures (dSR) and the distance to the epicenter (dEp) show significant and strong control effects, which are far stronger than the other five factors. Using only the dSR, dEp, engineering geological rock group (Eg), and the range, a particularly effective Poisson model of landslide density is constructed, whose area under the ROC (AUC) reaches 0.9244 and whose very high-density (VHD) zones can contain 50% of landslides and only comprise 3.9% of the study areas. This research not only deepens our understanding of the spatial distribution of WcEqTLs but also provides new technical methods for such investigation and analysis.