Building a landslide hazard indicator with machine learning and land surface models

被引:41
作者
Stanley, T. A. [1 ,2 ,3 ]
Kirschbaum, D. B. [3 ]
Sobieszczyk, S. [4 ]
Jasinski, M. F. [3 ]
Borak, J. S. [3 ,5 ]
Slaughter, S. L. [6 ]
机构
[1] Univ Space Res Assoc, Columbia, MD 21046 USA
[2] Goddard Earth Sci Technol & Res, Columbia, MD USA
[3] NASA, Goddard Space Flight Ctr, Hydrol Sci Lab, Greenbelt, MD USA
[4] US Geol Survey, Oregon Water Sci Ctr, Portland, OR USA
[5] Univ Maryland, Earth Syst Sci Interdisciplinary Ctr, College Pk, MD 20742 USA
[6] US Geol Survey, Landslide Hazards Program, Golden, CO USA
关键词
XGBoost; Washington; Oregon; Land data assimilation system; Gradient boosting machine; National climate assessment; SUSCEPTIBILITY; PREDICTION; RAINFALL; PRECIPITATION; SEASONALITY; WASHINGTON;
D O I
10.1016/j.envsoft.2020.104692
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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 19792016, 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.
引用
收藏
页数:15
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