Convolutional Neural Network-Based Risk Assessment of Regional Susceptibility to Road Collapse Disasters: A Case Study in Guangxi

The Guangxi Zhuang Autonomous Region, a vital strategic geographic entity in southern China, is prone to frequent road collapse disasters due to its complex topography and high rainfall, severely affecting regional economic and social development. Existing risk assessments for these collapse disasters often lack comprehensive analysis of the combined influence of multiple factors, and their predictive accuracy requires enhancement. To address these deficiencies, this study utilized the ResNet18 model, a convolutional neural network (CNN)-based approach, integrating 10 critical factors—including slope gradient, lithology, and precipitation—to develop a risk assessment model for road collapse disasters. This model predicts and maps the spatial distribution of collapse risk across Guangxi. The results reveal that very high-risk areas span 49,218.94 km2, constituting 20.38% of Guangxi’s total area, with a disaster point density of 8.67 per 100 km2; high-risk areas cover 56,543.87 km2, representing 23.41%, with a density of 3.38 per 100 km2; and low-risk areas encompass 61,750.69 km2, accounting for 25.57%, with a density of 0.29 per 100 km2. The receiver operating characteristic (ROC) curve yields an area under the curve (AUC) value of 0.7879, confirming the model’s high reliability and predictive accuracy in assessing collapse risk. This study establishes a scientific foundation for the prevention and mitigation of road collapse disasters in Guangxi and offers valuable guidance for risk assessments in similar regions.