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 250m 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.
