###### Abstract  
Geological disasters in large alpine reservoirs primarily take the form of landslide occurrences and are predominantly induced by slope instability. Presently, risk monitoring and assessment strategies tend to prioritize sudden alerts overlooking progressive trajectories from the onset of creeping deformations within the slope to its critical state preceding landslides. Hence, analyzing landslide safety risks over time demonstrates a significant degree of hysteresis, highlighting the necessity for a comprehensive approach to risk assessment that encompasses both gradual and sudden precursors to landslide events. This study analyzes the factors affecting slope stability and establishes a slope evaluation indicator system that includes terrain morphology, meteorological conditions, the ecological environment, soil conditions, human activity, and external manifestation. It proposes a quantitative model for slope landslide risk assessment based on a fuzzy broad learning system, aiming to accurately assess slopes with different risk levels. The overall assessment accuracy rate reaches 92.08%. This multi-dimensional risk assessment model provides long-term monitoring of slope conditions and scientific guidance on landslide risk management and disaster prevention and mitigation on a long time scale for risky slopes in reservoir areas. geologic hazards on reservoir slopes; landslide risk assessment; multi-dimensional evaluation indicator system; fuzzy broad learning system +
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nearby residents. Therefore, an accurate assessment of landslide risk in reservoir areas is crucial for maintaining the safety and efficiency of hydropower plants. The authors of [1] adopted a GPS high-precision positioning system to construct a real-time online dynamic slope monitoring system, with timely and accurate slope status monitoring and reliable data support for dynamic slope management. The authors of [2] applied a small baseline subset interferometric synthetic aperture radar to analyze slope deformation in the reservoir area of the Wudongde Hydropower Station. Their study correlated soil water content and reservoir water levels to understand slope stability, providing a valuable reference for slope stability analysis in a hydropower station's reservoir area. The authors of [3] employed multiple statistical indicators and interferometric synthetic aperture radar to conduct an accurate landslide risk assessment in Shenzhen. Their results provided technical and data support for landslide disaster prevention in this region. The authors of [4] proposed a landslide deformation monitoring method fusing terrestrial laser scanning and unmanned aerial vehicle photogrammetry, suitable for complex terrain conditions with access restrictions and blind areas. The above risk assessments of landslides have primarily focused on sudden changes caused by slope instability, but there are still deficiencies in monitoring the gradual and progressive processes of slopes, from creeping deformation to the critical state of landslides. This leads to a lag in analyzing the safety risk changes of landslide hazards and fails to provide long-term or effective support for landslide risk management decision making. The authors of [5] used the XGBoost algorithm to analyze 24 landslide impact factors and identified distance to roads, slope length, and lithology as the primary causative factors in urban areas, all of which increase the probability of landslide occurrence by about 10% compared with a single impact variable. The authors of [6] selected nine factors to predict the sensitivity of slope landslides and combined them with ArcGIS technology to make a dynamic risk assessment of the monitoring area. The authors of [7] evaluated landslide danger and susceptibility using twelve hazard factors and nine vulnerable evaluation factors.