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| Titel |
A satellite-based global landslide model |
| VerfasserIn |
A. Farahmand, A. AghaKouchak |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 13, no. 5 ; Nr. 13, no. 5 (2013-05-16), S.1259-1267 |
| Datensatznummer |
250018446
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| Publikation (Nr.) |
 copernicus.org/nhess-13-1259-2013.pdf |
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| Zusammenfassung |
| Landslides are devastating phenomena that cause huge damage around the
world. This paper presents a quasi-global landslide model derived using
satellite precipitation data, land-use land cover maps, and 250 m topography
information. This suggested landslide model is based on the Support Vector
Machines (SVM), a machine learning algorithm. The National Aeronautics and
Space Administration (NASA) Goddard Space Flight Center (GSFC) landslide
inventory data is used as observations and reference data. In all, 70% of the data
are used for model development and training, whereas 30% are used for
validation and verification. The results of 100 random subsamples of
available landslide observations revealed that the suggested landslide model
can predict historical landslides reliably. The average error of 100
iterations of landslide prediction is estimated to be approximately 7%, while
approximately 2% false landslide events are observed. |
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