Geohazard Susceptibility Modeling Under the Influence of Human Activities: A Case Study in Hunan Province, China

Landslides and collapses seriously threaten the safety of residents in Xiangtan County, Hunan Province, southern China. Local human engineering activities, dominated by slope cutting for housing construction, create steep artificial free faces, greatly weakening slope stability and becoming the key anthropogenic driver aggravating geological hazard risks, while targeted quantitative susceptibility assessment for residential slope units is still lacking for precise disaster prevention. To fill this gap and support proactive geohazard mitigation, this study selects Xiangtan County as the research object. A total of 166 landslide and collapse hazard points and 869 moderately and highly susceptible residential slope units were collected, and 12 conditioning factors, such as relative height difference, average slope and engineering rock mass group, were subsequently screened. Three hybrid intelligence models, namely PSO-BP, PSO-RF and PSO-SVM, were established to map residential slope unit susceptibility across the whole study area. The ROC-AUC and Kappa coefficient were adopted to quantify and compare the predictive performance of each model, and the Jenks natural breakpoint method combined with field survey data was used to classify all 7257 residential slope units into three susceptibility grades. The evaluation results show that the PSO-RF model performs best with an AUC of 0.913 and a Kappa coefficient of 0.64, representing strong predictive reliability. Under this optimal model, moderate-susceptibility units account for 12.94% (939 units) and high-susceptibility units account for 0.69% (50 units), both of which are concentrated in the southwest, southeast and partially northern zones of the county where intensive human slope-cutting activities prevail. This research provides a feasible technical framework for identifying high-risk residential slopes and delivers clear data support for local geohazard risk control and disaster reduction. In summary, the PSO-RF hybrid model is proven suitable for fine-scale susceptibility assessment of residential slopes in hilly regions with frequent small-sized slope failures.