Landslide Geometry Reveals its Trigger

Electronic databases of landslides seldom include the triggering mechanisms, rendering these inventories unusable for landslide hazard modeling. We present a method for classifying the triggering mechanisms of landslides in existing inventories, thus, allowing these inventories to aid in landslide hazard modeling corresponding to the correct event chain. Our method uses various geometric characteristics of landslides as the feature space for the machine-learning classifier random forest, resulting in accurate and robust classifications of landslide triggers. We applied the method to six landslide inventories spread over the Japanese archipelago in several different tests and training configurations to demonstrate the effectiveness of our approach. We achieved mean accuracy ranging from 67% to 92%. We also provide an illustrative example of a real-world usage scenario for our method using an additional inventory with unknown ground truth. Furthermore, our feature importance analysis indicates that landslides having identical trigger mechanisms exhibit similar geometric properties.

Automated summary

This study proposes a method to classify the triggering mechanisms of landslides in existing inventories, enabling their use in landslide hazard modeling by utilizing machine-learning classifier random forest with geometric characteristics as feature space. Application of the method to six landslide inventories showed high accuracy, and it was demonstrated that landslides with identical trigger mechanisms exhibit similar geometric properties.