Overcoming the data limitations in landslide susceptibility modelling
Links
- More information: Publisher Index Page (via DOI)
- Download citation as: RIS | Dublin Core
Abstract
Data-driven models widely used for assessing landslide susceptibility are severely limited by the landslide and environmental data needed to create them. They rely on inventories of past landslide locations, which are difficult to collect and often nonrepresentative. Furthermore, susceptibility maps are most needed in regions without the means to assemble an inventory. To overcome these challenges, we develop a method for assessing shallow landslide susceptibility based on a probabilistic morphometric analysis of the landscape’s topography, rather than the characteristics of landslides. The model assumes that hillslopes with higher relief and gradient compared to the surrounding landscape are more prone to landslides. We demonstrate the superior performance of this approach over contrasting data-driven models across the northwestern United States. As our morphometric model only requires elevation data, it overcomes the major limitations of data-driven models and facilitates the creation of effective susceptibility models in areas where it was previously unfeasible.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Overcoming the data limitations in landslide susceptibility modelling |
| Series title | Science Advances |
| DOI | 10.1126/sciadv.adt1541 |
| Volume | 11 |
| Issue | 8 |
| Year Published | 2025 |
| Language | English |
| Publisher | AAAS |
| Contributing office(s) | Geologic Hazards Science Center - Landslides / Earthquake Geology |
| Description | eadt1541, 13 p. |
| Country | United States |
| State | Oregon, Washington |
| Google Analytic Metrics | Metrics page |