Assessment of earthquake-triggered landslide susceptibility in El Salvador based on an artificial neural network model

Natural Hazards and Earth System Science

Volumn 10, Issue 6, 2010, Pages 1307-1315

  Garcia Rodriguez M J ^a   Malpica, J A ^b  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^b UNIVERSITY OF ALCALÁ   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; COMPARATIVE STUDY; EARTHQUAKE TRIGGER; GIS; HAZARD ASSESSMENT; LANDSLIDE; NUMERICAL MODEL; REGRESSION ANALYSIS; SLOPE STABILITY;

EL SALVADOR [CENTRAL AMERICA];

EID: 77954039583     PISSN: 15618633     EISSN: 16849981     Source Type: Journal    
DOI: 10.5194/nhess-10-1307-2010     Document Type: Article

References (35)

1. 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. (Pubitemid 40133436)
2. Band, L. E.: Spatial aggregation of complex terrain, Geogr. Anal., 21, 279-293, 1989.
3. Basheer, I. A. and Hajmeer, M.: Artificial neural networks: fundamentals, computing, design and application, J. Microbiol. Meth., 43, 3-31, 2000.
4. 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.
5. Bishop, C. M.: Neural Networks for Pattern Recognition, 1st edn., Oxford University Press, USA, 1996.
6. Chung, C. F. and Fabbri, A. G.: Probabilistic prediction models for landslide hazard mapping, Photogramm. Eng. Rem. S., 65, 1389-1399, 1999.
7. Chung, C. F. and Fabbri, A. G.: Validation of spatial prediction models for landslide hazard mapping, Nat. Hazards, 30, 451-472, 2003. (Pubitemid 38157959)
8. 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.
9. Dai, F. C. and Lee, C. F.: Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong, Geomorphology, 42, 213-228, 2002.
10. 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. (Pubitemid 43103328)
11. Ermini, L., Catani, F., and Casagli, N.: Artificial neural networks applied to landslide susceptibility assessment, Geomorphology, 66, 327-343, 2005. (Pubitemid 40350814)
12. 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.
13. Fawcett, T.: An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861-874, 2006.
14. 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.
15. 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.
16. 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. (Pubitemid 40382596)
17. Horn, B. K. P.: Hill-shading and the reflectance map, in: Proceedings of the IEEE, 69, 14-47, 1981.
18. Hosmer, D. W. and Lemeshow, S.: Applied Logistic Regression, 2nd edn., Wiley, New York, 2000.
19. Isaaks, E. H. and Srivastava, R. M.: An Introduction to Applied Geostatistics, Oxford University Press, Oxford, 1989.
20. 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. (Pubitemid 37261481)
21. 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. (Pubitemid 37515140)
22. 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. (Pubitemid 38127091)
23. 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. (Pubitemid 40659696)
24. McCulloch, W. S. and Pitts, W. H.: A logical calculus of the ideas immanent in nervous activity, B. Math. Biophys., 5, 115-133, 1943.
25. Mardia, K. V.: Statistics of directional data, Academic Press, London, 1972.
26. Montgomery, D. R., Schmidt, K. M., Greenberg, H. M., and Dietrich, W. E.: Forest clearing and regional landsliding, Geology, 28, 311-314, 2000.
27. Parker, D. B.: Learning logic, Technical report TR-47, Center for Computational Research in Economics and management Science, MIT, Cambridge, MA, 1985.
28. Pradhan, B. and Lee, S.: Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling, Environ. Modell. Softw., 25, 747-759, 2010.
29. Rossi, M., Guzzetti, F., Reichenbach, P., Mondini, A. and Peruccacci, S.: Optimal landslide susceptibility zonation based on multiple forecasts, Geomorphology, 114, 129-142, 2010.
30. Upton, G. J. G. and Fingleton, B.: Spatial Data Analysis by Example, John Wiley & Sons, Chichester, 1989.
31. Werbos, P.: Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, Ph.D. thesis, Harvard Cambridge, MA, 1974.
32. Wieczorek, G. F., Mandrone, G., and DeCola, L.: The influence of hillslope shape on debris-flowinitiation, in: Debrisflow Hazards Mitigation: Mechanics, Prediction, and Assessment American Society of Civil Engineers, edited by: Chen, C. L., New York, 21-31, 1997.
33. Yesilnacar, E. and Topal, T.: Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey), Eng. Geol., 79, 251-266, 2005. (Pubitemid 40876258)
34. Yilmaz, I.: Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey), Comput. Geosci., 35, 1125-1138, 2009.
35. Zhou, W.: Verification of the nonparametric characteristics of back-propagation neural networks for image classification, IEEE T. Geosci. Remote, 37, 771-779, 1999.