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This study reported the causes of prediction uncertainty in landslide susceptibility, based on machine learning. The landslide inventory and conditioning factors were first prepared. Thereafter, factor analysis, including the effectiveness, multicollinearity, and importance was performed for the subsequent susceptibility modeling. Finally, the predicted capacity was explored in terms of model performance and map performance. Investigation indicates that landslides are focused on the central and southeastern part of the study area, displaying the characteristics of small-scale and shallow sliding. The difference in the ML algorithm, kernel functions, the selection of base-classifier, the evaluation indicators of performance, and even the factor zonation results in difficulties in determining the optimal ML model. Besides, the DT-Boosting keeps the high AUC value, 0.981 and 0.862 respectively in the training and test phases, while the RF holds a highly robust in terms of fluctuation characteristics. Indeed, the susceptibility map generated by RBF-ANN can better capture the difference in landslide susceptibility, with the  D -value of 7.9. This study provides an in-depth understanding of the uncertainty of the predicted capacity in ML-based landslide susceptibility.

Exploring aspects affecting the predicted capacity of landslide susceptibility based on machine learning technology