Cross-Trigger Transferability of Run-out-Prediction Models for Rainfall- and Earthquake-Induced Landslides

Reliable prediction of landslide run-out distance is of great importance for hazard zoning and risk mitigation. However, most previous studies evaluate model performance within a single landslide inventory, while the transferability of models across different triggering mechanisms remains insufficiently explored. To evaluate whether landslide run-out-prediction models and their uncertainty estimates remain reliable when transferred between rainfall-induced and earthquake-induced landslide inventories, this study investigates trigger-dependent run-out behavior and cross-trigger transferability using a harmonized inventory of 10,158 rainfall-induced and 681 earthquake-induced records. Common geometric descriptors, including run-out distance L, elevation difference H, source area A, source volume V, and mean slope angle θ, were used for distributional comparison, scaling-law analysis, machine-learning prediction, tail-risk assessment, and uncertainty quantification. The results show that earthquake-induced landslides occupy a larger geometric domain, whereas rainfall-induced landslides exhibit greater elevation-normalized mobility. Cross-trigger prediction experiments reveal substantial and asymmetric transfer degradation, with systematic overprediction in R→E and underprediction in E→R. Prediction-interval reliability also deteriorates markedly under cross-trigger transfer, indicating that uncertainty estimates calibrated within one trigger type may not remain reliable when applied to another. These findings suggest that trigger-associated inventory differences should be explicitly considered in landslide run-out modeling. Direct application of models across rainfall- and earthquake-induced landslide inventories may lead to biased predictions and unreliable uncertainty estimates.