Building a landslide hazard indicator with machine learning and land surface models
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Abstract
The U.S. Pacific Northwest has a history of frequent and occasionally deadly landslides caused by various factors. Using a multivariate, machine-learning approach, we combined a Pacific Northwest Landslide Inventory with a 36-year gridded hydrologic dataset from the National Climate Assessment – Land Data Assimilation System to produce a landslide hazard indicator (LHI) on a daily 0.125-degree grid. The LHI identified where and when landslides were most probable over the years 1979–2016, addressing issues of bias and completeness that muddy the analysis of multi-decadal landslide inventories. The seasonal cycle was strong along the west coast, with a peak in the winter, but weaker east of the Cascade Range. This lagging indicator can fill gaps in the observational record to identify the seasonality of landslides over a large spatiotemporal domain and show how landslide hazard has responded to a changing climate.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Building a landslide hazard indicator with machine learning and land surface models |
| Series title | Environmental Modelling & Software |
| DOI | 10.1016/j.envsoft.2020.104692 |
| Volume | 129 |
| Year Published | 2020 |
| Language | English |
| Publisher | Elsevier |
| Contributing office(s) | Oregon Water Science Center |
| Description | 104692, 15 p. |
| Country | United States |
| State | Oregon, Washington |
| Google Analytic Metrics | Metrics page |