Risk assessment of geologic hazards in county-slope units based on RF-AER-AHP combined models

As among the most destructive types of natural hazards, geological disasters pose serious threats to human life, property safety, and socioeconomic development. Accurate identification and assessment of geological disaster risk are crucial for revealing the spatial distribution and underlying mechanisms of disasters and for providing scientific support for regional disaster prevention and territorial spatial planning. However, traditional risk assessment methods often suffer from high subjectivity in weight determination, limited model precision, and insufficient coupling among influencing factors, making it difficult to capture the complexity of disaster risk. To address these limitations, in this study, a coupled random forest–assumption of extreme rainfall–analytic hierarchy process (RF–ARE–AHP) model based on slope units for county-level geological disaster risk assessment was developed, using Ningyuan County in Hunan Province, China, as a case study. Model performance was validated using the area under the ROC curve (AUC), confusion matrix parameters, and field investigation results, and a high-resolution risk zoning map was generated at the slope-unit scale. The results show that the susceptibility model achieved an AUC of 0.92, with true positive, true negative, and overall accuracy rates exceeding 85% and false rates below 15%, indicating high model reliability and discriminative capacity. The hazard of geological disasters increased significantly with increasing rainfall intensity, expanding both the spatial extent and the severity of high-hazard areas. In the exposure assessment, the introduction of a damping coefficient effectively differentiated the exposure levels of the threatened elements. The medium–high-risk zones were concentrated mainly in Jiuyi Mountain, Mianhuaping, and Tongmulou Yao ethnic townships, which is consistent with field observations. Overall, the proposed RF–ARE–AHP model reduces subjectivity and enhances accuracy, providing a robust and scalable approach for county-level geological disaster risk evaluation and disaster prevention planning.