Task-Specific Negative Sample Selection for Multi-Hazard Susceptibility Mapping of Slope-Instability Hazards

AI-based multi-hazard susceptibility mapping has become an important approach for identifying slope-instability hazards over large areas. However, in multi-hazard modeling, negative samples do not have a consistent stable meaning across hazard types: a background unit that is stable for one hazard may correspond to a potentially susceptible environment for another. Existing studies mainly focus on model optimization and accuracy improvement, but pay limited attention to task-level label confusion and background-sample mismatch caused by this issue. To address this gap, this study proposes a task-specific negative sample selection method based on the feature-space distribution of hazard-specific positive samples. Specifically, for each hazard type, the method uses its positive-sample centroid as a reference to rank the shared candidate background pool by feature-space distance. Rather than selecting only hard or easy negatives, it combines hard negatives closer to the centroid, easy negatives farther from it, and randomly supplemented background units to improve diversity. Taking the Minjiang River Basin in southeastern coastal China as the study area, the proposed method was compared with five benchmark strategies—random sampling, easy negative sampling, hard negative sampling, outside-buffer sampling, and buffer-ring sampling—using random forest (RF), boosted regression tree (BRT), multilayer perceptron (MLP), support vector machine (SVM), and logistic regression (LR). The results show that the dataset constructed using the proposed method improves the overall classification performance of the models, with average improvements of 14.47% and 12.25% in AUC and F1-score, respectively. The corresponding susceptibility maps also show the highest spatial screening efficiency, with the top 10% susceptible areas capturing 39.26% of historical hazard events, and only 27.06% of slope units were required to cover 80% of the hazards. In addition, this study further discusses the influence of hazard-specific adaptability on the feature-space distribution of negative samples, as well as the relationships among negative-sample selection strategies, classification performance, and study area-wide susceptibility-map quality. This study provides a new method for constructing reliable negative samples in regional multi-hazard susceptibility mapping of slope-instability hazards.