A satellite-based global landslide model

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

Landslide susceptibility mapping on a global scale using the method of logistic regression

“…4 Ercanoglu and Gokceoglu (2002). 5 Lee and Min (2001 Farahmand and AghaKouchak (2013). 12 Nadim et al (2006).…”

“…At a global scale, factors such as elevation and slope gradient can be replaced by topographic index or relative relief, which indicate macroscopic differences in topography. Especially for landslide data with low location precision, using factors such as elevation or slope gradient that precisely relate to landslide location will reduce the accuracy of landslide susceptibility analysis (Farahmand and AghaKouchak, 2013). Therefore, a general factor such as relative relief is more appropriate, and in this paper, relative relief is used to represent topography.…”

“…With the development of large data-sharing frameworks for meteorological data, precipitation information is easily available and hence frequently used in landslide analysis (Farahmand and AghaKouchak, 2013). However, as Nadim et al (2006) propose, soil moisture can also be a proxy of the water condition for it represents the average moisture condition of the soil.…”

“…Some scholars have also attempted to study global landslides with statistical methods. Farahmand and AghaKouchak (2013) used a global landslide inventory compiled by the National Aeronautics and Space Administration (NASA) to build a global landslide susceptibility model based on the use of a support vector machine (SVM), which includes three variables, satellite-sensed precipitation, digital elevation model (DEM) and land cover type. Compared with some complex numerical methods like SVM, logistic regression provides a simple method to produce a global landslide susceptibility map, which would be helpful in disseminating this research and could encourage further model development for its simplicity in modeling.…”

Landslides and synoptic weather trends in the European Alps

“…For landslides, the hydro-meteorological trigger is often rainfall (Jakob and Weatherly 2003;Farahmand and Aghakouchak 2013), and empirical rainfall thresholds are often used to define minimum triggering conditions for landslides (Peruccacci et al 2012); however these are often localized, and depend greatly on the quality of rainfall data (Gariano et al 2015). The use of broader scale atmospheric conditions can alleviate this problem although, to date, few studies have addressed this issue (e.g.…”