###### Abstract  
Current sampling methods do not provide effective quantitative assessment mechanisms for evaluating the intrinsic credibility of negative samples. This impedes the systematic quantification of the effect of misselection of geologically predisposed areas (i.e., potential landslide zones) as negative samples on the accuracy of landslide susceptibility evaluation models. To overcome this challenge, this study proposes a fuzzy membership-based sampling method for assessing negative sample credibility in the Liangshan Yi Autonomous Prefecture, where credibility is defined as the confidence level of stable nonlandslide samples. Subsequently, negative samples were sampled across stratified credibility thresholds to construct a frequency ratio-random forest coupled model. The influence of negative sample credibility on model performance was then systematically evaluated using various metrics, including the F1-score (metrics for evaluating classification performance), area under the receiver operating characteristic curve (AUC), and actual landslide distribution ratio (landslide proportion) in high-susceptibility zones. The results are as follows: (1) Increasing the credibility threshold progressively improves model precision while inducing systematic overestimation bias in regional susceptibility assessment; (2) Integrated analysis of model performance and landslide distribution characteristics (where recall, F1-score, and AUC values initially increase then decrease) confirms the optimal effectiveness when selecting negative samples within a credibility threshold range of 0.7-1.0. This study innovatively achieves quantitative optimization of negative samples and provides a universal solution for improving the performance of diverse models reliant on negative sampling strategies. l 2025 15 7646 10.3390/app1514764 10.  
ear mapping capabilities. However, their application efficacy is significantly constrained by training data quality, particularly the credibility of negative samples [3; 4]. The credibility of negative samples, which represent confidence levels in stable nonlandslide instances, constrains the precision and accuracy of ML models [4]. However, no standardized criteria for selecting scientifically sound negative samples have been established. Based on method similarities, existing sampling approaches can be categorized into four distinct types. Four primary methodologies for negative sample selection have been established based on geographical similarities in sampling mechanisms: (1) random sampling, involving stochastic selection across the study area, ensures uniform spatial distribution devoid of subjective bias but risks contamination by latent landslide instances, i.e., spatial locations exhibiting predisposing conditions for slope failure without historical occurrences, thereby compromising interclass discriminability [5]; (2) buffer-controlled sampling selects samples beyond predefined perimeters around landslides, leveraging environmental dissimilarity while introducing subjectivity through arbitrary buffer distances and neglecting regional geomorphological homogeneity [6; 7]; (3) model-coupled sampling derives negative samples from low-susceptibility zones predicted by negative-sample-independent models or via physical modeling-based slope safety factors, thereby achieving superior prediction accuracy though constrained by computational inefficiency at scale [8]; (4) environmental factor-constrained sampling employs single/multiple constraints (e.g., low-gradient zones, extrapolation sampling), offering high efficiency and generalizability but suffering from either overemphasis on specific factors (single-constraint) or stochastic noise introduction (multi-constraint) that degrades sample integrity [9].