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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 13, issue 5
Nat. Hazards Earth Syst. Sci., 13, 1259–1267, 2013
https://doi.org/10.5194/nhess-13-1259-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: New developments and applications in early warning, monitoring...

Nat. Hazards Earth Syst. Sci., 13, 1259–1267, 2013
https://doi.org/10.5194/nhess-13-1259-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 16 May 2013

Research article | 16 May 2013

A satellite-based global landslide model

A. Farahmand and A. AghaKouchak A. Farahmand and A. AghaKouchak
  • University of California Irvine, Irvine, CA 92697, USA

Abstract. 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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