Enhancing Multi-Geohazard Susceptibility Modeling Through Extreme Precipitation Indicators and Spatially Constrained Negative Sample Selection: A Case Study from Shanxi Province, China

Abstract

Loess mountainous regions in northern China suffer frequent landslides, collapses and debris flows controlled by complex geological settings, seasonal rainstorms, freeze–thaw cycles and large-scale human engineering activities. Multi-geohazard susceptibility evaluation can provide fundamental data support for regional disaster prevention and territorial planning. Taking Shanxi Province, a typical loess-mountain transition zone, as the study area, this paper establishes an evaluation framework for landslides, collapses and debris flows. Ten conditioning factors are selected, including lithology, terrain parameters, distance to faults, distance to rivers, NDVI and RX1day (annual maximum 1-day precipitation). A 30 m grid unit is adopted as the basic evaluation unit. A total of 2598 verified geohazard points are taken as positive samples. Negative samples with equal quantity are extracted from low and very low susceptibility areas of the preliminary zoning map generated by the Frequency Ratio (FR) method, with an 800 m minimum separation distance between sampling points to reduce spatial autocorrelation. Two models, Logistic Regression (LR) and Support Vector Machine (SVM), are constructed, and five-fold cross-validation is used to test model performance through five statistical indicators and AUC values. The results show that, under the specific model configurations and sampling strategy adopted in this study, the LR model achieved higher predictive performance (average test AUC = 0.995) than the SVM model (average test AUC = 0.752) in the comparative assessment. Statistical analysis of the final susceptibility map derived from the LR model indicates that high and very high susceptibility zones account for 80.94% of the total provincial area and contain 87.45% of all recorded geohazard points, which confirms the consistency and reasonableness of the zoning results. Spatially, high-susceptibility areas are concentrated in the western and northwestern loess tablelands, the Fenhe River fault basin, and fault-developed sections of the Lüliang and Taihang Mountains. Thick loess layers, river undercutting and coal mining activities jointly reduce slope stability in these zones. Compared with conventional susceptibility modeling workflows, this study incorporates the RX1day extreme precipitation index and implements Frequency-Ratio-constrained stratified negative-sample selection to reduce training-sample bias. The produced susceptibility maps can provide technical support for differentiated geological hazard risk management, ecological restoration and territorial spatial planning for loess-mountain transition regions in northern China, thereby directly contributing to regional sustainable development and disaster resilience.