Assessment of earthquake- triggered landslides in El Salvador based on an Artificial Neural Networks (ANN) model

García Rodríguez, María José and Benito Oterino, Belen and Malpica Velasco, José Antonio (2008). Assessment of earthquake- triggered landslides in El Salvador based on an Artificial Neural Networks (ANN) model. In: "European GeoSciencies Union General Assembly 2008", 13/04/2008-18/04/2008, Viena, Austria. ISBN 1607-7962.

Description

Title: Assessment of earthquake- triggered landslides in El Salvador based on an Artificial Neural Networks (ANN) model
Author/s:
  • García Rodríguez, María José
  • Benito Oterino, Belen
  • Malpica Velasco, José Antonio
Item Type: Presentation at Congress or Conference (Article)
Event Title: European GeoSciencies Union General Assembly 2008
Event Dates: 13/04/2008-18/04/2008
Event Location: Viena, Austria
Title of Book: Geophysical Research Abstracts
Date: April 2008
ISBN: 1607-7962
Volume: 10
Subjects:
Faculty: E.T.S.I. en Topografía, Geodesia y Cartografía (UPM)
Department: Ingeniería Topográfica y Cartografía [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Landslide hazard studies are appropriate for evaluation and mitigation plan development in potential areas for the occurrence of landslides. There are several techniques available for landslide hazard research, however statistical approaches provide rigorous analysis to assess landslide hazard over large regions. This study is an approach for assessing earthquake-triggered landslides hazard using artificial neural networks (ANN). The computational method 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 in January 13th (Mw 7.7) and February 13th, 2001, (Mw 6.6). The first of these earthquakes triggered more the 500 landslides, included the most tragic in Las Colinas landslide, and killed at least 844 people.

More information

Item ID: 3487
DC Identifier: http://oa.upm.es/3487/
OAI Identifier: oai:oa.upm.es:3487
Official URL: http://www.cosis.net/abstracts/EGU2008/09822/EGU2008-A-09822.pdf
Deposited by: Memoria Investigacion
Deposited on: 01 Jul 2010 11:14
Last Modified: 20 Apr 2016 13:03
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