TY  - JOUR
A1  - Rana, Kamal
A1  - Öztürk, Ugur
A1  - Malik, Nishant
T1  - Landslide geometry reveals its trigger
T2  - Geophysical research letters : GRL / American Geophysical Union
N2  - 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.
KW  - databases
KW  - Japan | landslides
KW  - random forest
Y1  - 2021
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/63933
SN  - 0094-8276
SN  - 1944-8007
VL  - 48
IS  - 4
PB  - American Geophysical Union
CY  - Washington
ER  -