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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
Publication Date:
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

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.

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

Ayalew, L. and Yamagishi, H.: The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan, Geomorphology, 65, 15–31, 2005.; Band, L. E.: Spatial aggregation of complex terrain, Geogr. Anal., 21, 279–293, 1989.; Basheer, I. A. and Hajmeer, M.: Artificial neural networks: fundamentals, computing, design and application, J. Microbiol. Meth., 43, 3–31, 2000.; Benito, B., Cepeda, J. M., and Martínez Díaz, J. J.: Analysis of the spatial and temporal distribution of the 2001 earthquakes in El Salvador, in: Geological Society of America Special Paper~375, Natural Hazards in El Salvador, edited by: Rose, W. I., Bommer, J. J., López, D. L., Carr, M. J., and Major, J. J., Boulder, Colorado, 339–356, 2004.; Bishop, C. M.: Neural Networks for Pattern Recognition, 1st edn., Oxford University Press, USA, 1996.; Chung, C. F. and Fabbri, A .G.: Probabilistic prediction models for landslide hazard mapping, Photogramm. Eng. Rem. S., 65, 1389–1399, 1999.; Chung, C. F. and Fabbri, A. G.: Validation of spatial prediction models for landslide hazard mapping, Nat. Hazards, 30, 451–472, 2003.; Cruden, D. M. and Varnes, D. J.: Landslides Types and Processes, in: Landslides: investigation and mitigation, Special Report 247, En Transportation Research Board, National Research Council, edited by: Turner, A. K. and Shuster, R. L., Washintong, DC, 36–71, 1996.; Dai, F. C. and Lee, C. F.: Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong, Geomorphology, 42, 213–228, 2002.; Ercanoglu, M.: Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey) by artificial neural networks, Nat. Hazards Earth Syst. Sci., 5, 979–992, doi:10.5194/nhess-5-979-2005, 2005.; Ermini, L., Catani, F., and Casagli, N.: Artificial neural networks applied to landslide susceptibility assessment, Geomorphology, 66, 327–343, 2005.; Falaschi, F., Giacomelli, F., Federici, P. R., Puccinelli, A., D'Amato Avanzi, G., Pochini, A., and Ribolini, A.: Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy, Nat. Hazards, 50, 551–569, 2009.; Fawcett, T.: An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861–874, 2006.; García-Rodríguez, M. J., Malpica, J. A., Benito, B., and Díaz, M.: Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression, Geomorphology, 95, 172–191, 2008.; García-Rodríguez, M. J.: Metodologias para la evaluación de peligrosidad a los deslizamientos inducidos por terremotos, Ph.D thesis, Universidad de Alcalá, España, 300~pp., 2009.; Gómez, H. and Kavzoglu, T.: Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela, Eng. Geol., 78, 11–27, 2005.; Horn, B. K. P.: Hill-shading and the reflectance map, in: Proceedings of the IEEE, 69, 14-47, 1981.; Hosmer, D. W. and Lemeshow, S.: Applied Logistic Regression, 2nd edn., Wiley, New York, 2000.; Isaaks, E. H. and Srivastava, R. M.: An Introduction to Applied Geostatistics, Oxford University Press, Oxford, 1989.; Lee, S., Ryu, J. H., Lee, M. J., and Won, J. S.: Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea, Environ. Geol., 44, 820–833, 2003a.; Lee, S., Ryu, J. H., Min, K., and Won, J. S.: Landslide susceptibility analysis using GIS and artificial neural network, Earth Surf. Proc. Land., 28, 1361–1376, 2003b.; Lee, S., Ryu, J. H., Won, J. S., and Park, H. J.: Determination and application of the weights for landslide susceptibility mapping using an artificial neural network, Eng. Geol., 71, 289–302, 2004.; Lee, S.: Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data, Int. J. Remote Sens., 26, 1477–1491, 2005.; McCulloch, W. S. and Pitts, W. H.: A logical calculus of the ide


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