# Monitoring and Analysis of Slope Geological Hazards Based on UAV Images
## 5 Geographic Information Interpretation and Analysis
### Stability Analysis  
Investigating a variety of slope stability evaluation models [44; 45; 46; 47; 48; 49], this paper uses the Analytic Hierarchy Process (AHP) to calculate the weights of each evaluation factor of the slope. AHP is a multi-criteria decision-making tool used for the systematic evaluation and prioritization of complex problems. It is particularly applicable in scenarios that require the integration of both qualitative and quantitative factors, spanning fields such as engineering management, urban planning, environmental sciences, supply chain and logistics, healthcare, finance and investment, marketing, and more. Its core strength lies in combining expert judgment and objective data through a structured model, effectively reconciling multiple conflicting interests [50]. This paper finally adopts the hazard evaluation model to assess the stability of the slope, and its evaluation model is as follows. \[D_{L}=\sum_{i=1}^{n}W_{i}\cdot I_{i}, \tag{3}\]  
Figure 11: Curvature diagram: (**a**) Phase I curvature diagram; (**b**) Phase II curvature diagram. where \(D_{L}\) represents the slope hazard index, \(W_{i}\) represents the weight vector of the discriminant factor, and \(I_{i}\) represents the action index of the discriminant factor. Based on field investigations and research by other scholars [51; 52; 53], the slope stability evaluation index hierarchy is constructed according to three levels: the target level (A), the class index level (B), and the base index level (C). The factors influencing slope stability are divided into intrinsic and extrinsic categories. Intrinsic factors include geological conditions (B1) and topography and geomorphology (B2), while extrinsic factors include triggering factors (B3) and human activities (B4), resulting in a total of four primary categories of evaluation indicators. Under geological conditions (B1), secondary evaluation indicators include the type of geotechnical body (C1), structural surface density (C2), and degree of weathering (C3). Under topography and geomorphology (B2), secondary evaluation indicators include slope gradient (C4), slope direction (C5), curvature (C6), and vegetation cover (C7). Under triggering factors (B3), secondary evaluation indicators include average annual rainfall (C8) and groundwater impact (C9). Human activities (B4) include secondary indicators such as anthropogenic disturbances (C11) and vegetation damage (C12). The Saaty 1-9 scale method was employed to conduct the expert questionnaire [54], with a total of eight experts participating. The Saaty 1-9 scale offers greater sensitivity in distinguishing subtle differences compared to the 1-5 or 1-7 scales, while also avoiding the confusion that may arise from excessive granularity. The consistency ratio, calculated using the maximum eigenvalue method, has been shown to be statistically responsive to inconsistencies in the judgment matrix when using the 1-9 scale, effectively identifying logical contradictions. A comparative study further demonstrated that the correlation coefficient between AHP results using the 1-9 scale and those derived from the Fuzzy Comprehensive Evaluation (FCE) method reached 0.91, confirming the engineering applicability of the approach [55; 56]. The eight scoring experts possessed extensive experience and academic expertise in geotechnical engineering, slope stability analysis, and risk assessment. The panel comprised four geotechnical engineers, three surveying and mapping engineers, and one infrastructure management engineer. All experts had prior experience with multi-criteria decision-making frameworks and were well-versed in the Saaty 1-9 scale method. The individual judgment matrices were aggregated using the geometric mean method to construct a matrix of first-level evaluation indicators, as shown in Table 3. The use of geometric means to aggregate expert judgments mitigates the influence of extreme opinions while maintaining the proportional relationships among the indicators.