Mapping landslide susceptibility over large regions with limited data

Journal of Geophysical Research: Earth Surface
By: , and 



Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are used to mitigate this challenge. However, to our knowledge, no study has systematically evaluated how different sampling strategies alter a model's predictor effects (i.e., how a predictor value influences the susceptibility output) critical to explaining differences in model outputs. Here, we introduce a statistical framework that examines the variation in predictor effects and the model accuracy (measured using receiver operator characteristics) to highlight why certain sampling strategies are more effective than others. Specifically, we apply our framework to an array of logistic regression models trained on landslide inventories collected at sub-regional scales over four terrains across the United States. Results show significant variations in predictor effects depending on the inventory used to train the models. The inconsistent predictor effects cause low accuracies when testing models on inventories outside the domain of the training data. Grouping test and training sets according to physiographic and ecological characteristics, which are thought to share similar triggering mechanisms, does not improve model accuracy. We also show that using limited landslide data distributed uniformly over the entire modeling domain is better than using dense but spatially isolated data to train a model for applications over large regions.

Study Area

Publication type Article
Publication Subtype Journal Article
Title Mapping landslide susceptibility over large regions with limited data
Series title Journal of Geophysical Research: Earth Surface
DOI 10.1029/2022JF006810
Volume 128
Issue 5
Year Published 2023
Language English
Publisher American Geophysical Union
Contributing office(s) Geologic Hazards Science Center, Western Geographic Science Center
Description e2022JF006810, 21 p.
Country United States
State California, Kentucky, North Carolina, West Virginia
County Doddridge County, Macon County, Magoffin County
Other Geospatial Elkhorn Ridge Wilderness
Google Analytic Metrics Metrics page
Additional publication details