Natural Hazards and Earth System Science www.nat-hazards-earth-syst-sci.net 1561-8633 1684-9981 10 6 2010 10.5194/nhess-10-1307-2010 http://www.nat-hazards-earth-syst-sci.net/10/1307/2010/ http://www.nat-hazards-earth-syst-sci.net/10/1307/2010/nhess-10-1307-2010.html http://www.nat-hazards-earth-syst-sci.net/10/1307/2010/nhess-10-1307-2010.pdf 1307 1315 2010-06-25 Assessment of earthquake-triggered landslide susceptibility in El Salvador based on an Artificial Neural Network model M. J. García-Rodríguez J. A. Malpica Universidad Politécnica de Madrid, Escuela Técnica Superior de Ingenieros en Topografía, Geodesia y Cartografía (ETSITGC), Departamento de Ingeniería Topográfica y Cartografía, Madrid, Spain Universidad de Alcalá, Escuela Politécnica, Departamento de Matemáticas, Madrid, Spain This paper presents an approach for assessing earthquake-triggered landslide susceptibility using artificial neural networks (ANNs). The computational method used for the training process is a back-propagation learning algorithm. It is applied to El Salvador, one of the most seismically active regions in Central America, where the last severe destructive earthquakes occurred on 13 January 2001 (<i>M</i>_w 7.7) and 13 February 2001 (<i>M</i>_w 6.6). The first one triggered more than 600 landslides (including the most tragic, Las Colinas landslide) and killed at least 844 people. <br><br> The ANN is designed and programmed to develop landslide susceptibility analysis techniques at a regional scale. This approach uses an inventory of landslides and different parameters of slope instability: slope gradient, elevation, aspect, mean annual precipitation, lithology, land use, and terrain roughness. The information obtained from ANN is then used by a Geographic Information System (GIS) to map the landslide susceptibility. 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