Abstract Results from stochastic models depend on the variability of the input dataset. This is particularly relevant in data-driven landslide susceptibility models, where inputs can vary depending on the methodological approach. Therefore, understanding the assumptions and limitations of these models is essential for accurately interpreting results and supporting land-use planning. This study explored uncertainties in landslide susceptibility modeling (LSM) resulting from the random division of input data, using an inventory of 304 landslides. The framework included nine machine learning methods, 100 random resampling iterations with 10-fold cross-validation, and four validation metrics: Accuracy (ACC), Area Under the ROC Curve (AUROC), False Positive Rate (FPR), and False Negative Rate (FNR). Statistical Significance Tests (SST) compared performance between methods. On average, the Random Forest (RF) emerged as the most efficient algorithm, achieving an AUROC of 0.945, ACC of 88.15%, FNR of 11.13%, and FPR of 12.58%. It was followed by Artificial Neural Networks (ANN), with AUROC = 0.939, ACC = 87.68%, FNR = 11.70%, and FPR = 12.95%. The K-Nearest Neighbors (KNN) also showed strong results, with AUROC of 0.929, ACC of 85.73%, FNR of 14.40%, and FPR of 14.14%. These three methods demonstrated more stable validation metrics, suggesting potential to reduce bias and variance. In contrast, Decision Tree (DT), Support Vector Machine (SVM), and Rule Learning (RL) showed higher variability and poorer performance. The SST confirmed RF as the most effective method, followed by ANN and KNN. However, the RF model exhibited a substantial decrease in predictive performance when evaluated using spatial rather than random cross-validation.
Barella et al. (Mon,) studied this question.