## 2 Materials and Methods
### Research Methods
#### 2.3.2 Zoning Method  
The disaster-inducing environment of geological hazards on the Qinghai-Tibet Plateau exhibits pronounced spatial heterogeneity, with its formation mechanisms governed by the coupling of multiple factors, including topography, geological structures, lithology,hydroology, and climate [34; 35]. The scientific zoning of such environments is essential for identifying dominant hazard-inducing factors and elucidating the mechanisms underlying hazard development. Based on physical geography systems theory and spatial analysis methodologies, this study develops a \"multi-factor-multi-perspective-multi-scale\" technical approach to hazard-inducing environment zoning. This approach integrates both qualitative and quantitative analyses to reveal spatial heterogeneity in factor distributions and corresponding hazard patterns [26].  
The purpose of hazard-inducing environment zoning is to support geological hazard susceptibility assessments by ensuring consistency in formation mechanisms within zones and highlighting significant differences between zones. This study adheres to three fundamental principles: first, the principle of spatial correlation and heterogeneity, which groups spatially clustered or characteristic-consistent factors into the same zone, while significantly different ones are divided into different zones; second, the principle of comprehensiveness and independence, which integrates the response relationships between multiple factors and hazards while maintaining independent analysis of the spatial structure of factors; and third, the principle of specificity, which identifies regional characteristics that conventional factors cannot reveal through field surveys and the literature, thereby enhancing the scientific validity and applicability of the zoning.  
Following these principles, this study establishes a technical workflow based on a \"factor-driven, model-supported, GIS-based weighted overlay\" strategy. First, key hazard-inducing factors--including landform types, geological structures, lithology, and precipitation--are selected to construct a standardized input dataset. Geological hazard points (landslides, collapses, and debris flows) from 2000 to 2020 are compiled into a spatial hazard database. Second, on the ArcGIS platform, the spatial relationships between hazard points and environmental factors are analyzed, with indicators such as hazard density and frequency extracted to identify each factor's response characteristics. Third, a Random Forest model is employed to quantify the contribution of each factor, calculate corresponding weights, and generate a composite evaluation layer through weighted overlay analysis. Subsequently, spatial zones exhibiting significant heterogeneity are extracted. Finally, a hazard-inducing environment zoning map is produced. This methodology enables the systematic identification of dominant factors and regional disparities. It also incorporates FEM-based assessments of subsurface environmental structures [36], offering a scientific basis for analyzing hazard formation mechanisms and guiding disaster prevention and mitigation strategies [37].