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Assessment of Earthquake-triggered Landslide Susceptibility in El Salvador Based on an Artificial Neural Network Model : Volume 10, Issue 6 (25/06/2010)

By García-rodríguez, M. J.

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Book Id: WPLBN0003972534
Format Type: PDF Article :
File Size: Pages 9
Reproduction Date: 2015

Title: Assessment of Earthquake-triggered Landslide Susceptibility in El Salvador Based on an Artificial Neural Network Model : Volume 10, Issue 6 (25/06/2010)  
Author: García-rodríguez, M. J.
Volume: Vol. 10, Issue 6
Language: English
Subject: Science, Natural, Hazards
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2010
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Malpica, J. A., & García-Rodríguez, M. J. (2010). Assessment of Earthquake-triggered Landslide Susceptibility in El Salvador Based on an Artificial Neural Network Model : Volume 10, Issue 6 (25/06/2010). Retrieved from http://www.worldebookfair.com/


Description
Description: Universidad Politécnica de Madrid, Escuela Técnica Superior de Ingenieros en Topografía, Geodesia y Cartografía (ETSITGC), Departamento de Ingeniería Topográfica y Cartografía, 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 (Mw 7.7) and 13 February 2001 (Mw 6.6). The first one triggered more than 600 landslides (including the most tragic, Las Colinas landslide) and killed at least 844 people.

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. In a previous work, a Logistic Regression (LR) was analysed with the same parameters considered in the ANN as independent variables and the occurrence or non-occurrence of landslides as dependent variables. As a result, the logistic approach determined the importance of terrain roughness and soil type as key factors within the model. The results of the landslide susceptibility analysis with ANN are checked using landslide location data. These results show a high concordance between the landslide inventory and the high susceptibility estimated zone. Finally, a comparative analysis of the ANN and LR models are made. The advantages and disadvantages of both approaches are discussed using Receiver Operating Characteristic (ROC) curves.


Summary
Assessment of earthquake-triggered landslide susceptibility in El Salvador based on an Artificial Neural Network model

Excerpt
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