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Building a landslide hazard indicator with machine learning and land surface models
Environmental Modelling & Software ( IF 4.552 ) Pub Date : 2020-03-13 , DOI: 10.1016/j.envsoft.2020.104692
T.A. Stanley; D.B. Kirschbaum; S. Sobieszczyk; M. Jasinski; J. Borak; S. Slaughter

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.
更新日期:2020-03-16

 

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