A neural network model applied to landslide susceptibility analysis (Capitanejo, Colombia)

References

• Akgun A. 2012. A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides. 9:93–106.
   Google Scholar
• Akgun A, Kıncal C, Pradhan B. 2012a. Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey). Environ Monit Assess. 184:5453–5470.
   Google Scholar
• Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B. 2012b. An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci. 38:23–34.
   Google Scholar
• Alcantara-Ayala I. 2002. Geomorphology, natural hazards, vulnerability and prevention of natural disasters in developing countries. Geomorphology. 47:107–124.
   Google Scholar
• Aleotti P, Chowdhury R. 1999. Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ. 58:21–44.
   Google Scholar
• Anderson D, McNeill G. 1992. Artificial neural networks technology. Utica: Kaman Sciences Corporation.
   Google Scholar
• ASF. Dataset: ASF DAAC 2015, ALOS PALSAR_Radiometric_Terrain_Corrected_high_res. Includes Material © JAXA/METI 2007. doi:10.5067 / Z97HFCNKR6VA. (Accessed 2016 November 12).
   Google Scholar
• Ayala FJ. 1987. Introduction to Geological Risk. IGME (Instituto Geologico y Minero de España. ), Serie Geologıa Ambiental, Madrid; p. 3–19. (reference in Spanish)
   Google Scholar
• Ayala-Carcedo F, Cantos J. 2002. Natural risks. Barcelona, España: Ariel. (reference in Spanish)
   Google Scholar
• Ayalew L, Yamagishi H. 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda- Yahiko Mountains, Central Japan. Geomorphology. 65:15–31.
   Google Scholar
• Bathrellos GD, Gaki-Papanastassiou K, Skilodimou HD, Papanastassiou D, Chousianitis KG. 2012. Potential suitability for urban planning and industry development by using natural hazard maps and geological – geomorphological parameters. Environ Earth Sci. 66: 537–548.
   Google Scholar
• Bathrellos GD, Skilodimou HD, Chousianitis K, Youssef AM, Pradhan B. 2017. Suitability estimation for urban development using multi-hazard assessment map. Sci Total Environ. 575:119–134.
   Google Scholar
• Bhardwaj A, Venkatachalam G. 2014. Landslide hazard evaluation using artificial neural networks and GIS. In Landslide science for a safer geoenvironment. Cham: Springer; p. 397–403.
   Google Scholar
• Boinet T, Bourgois J, Mendoza H. 1989. The Fault of Bucaramanga (Colombia), its function during the Andean Orogeny. Geol Norandina. 11:3–10. (Reference in Spanish).
   Google Scholar
• Botzen WJ, Aerts J, Van den Bergh J. 2013. Individual preferences for reducing flood risk to near zero through elevation. Mitig Adapt Strateg Global Change. 18:229–244.
   Google Scholar
• Campbell CJ. 1965. The Santa Marta wrench fault of Colombia and its regional setting. Fourth Caribbean Geological Conference. Trinidad. Memoir 247–261.
   Google Scholar
• Cardona OD, Hurtado JE, Duque G, Moreno A, Chardon AC, Velasquez LS, Prieto SD. 2004. Relative risk dimensioning and management: Methodology using indicators at the national level. BID/IDEA. Recovered on November 16, 2016: http://idea.unalmzl.edu.co/documentos/02%20Fundamentos%20Metodologicos%20Indicadores%20BID-IDEA%20Fase%20I.pdf.
   Google Scholar
• Carrara A, Cardinali M, Detti M, Guzzetti F, Pasqui V, Reichenbach P. 1991. GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Process Landf. 16:427–445.
   Google Scholar
• Cediel F, Shaw RP, Caceres C. 2003. Tectonicassembly of the North Andean block. In: Bartolini C, Bufler RT, Blickwede J, editors. The Circum Gulf of Mexico and the Caribbean: Hydrocarbon Habitats, Basin Formations and Plate Tectonics. American Association of Petroleum Geologists, Memoirs 79:815–848.
   Google Scholar
• CEPAL. 2012. Valuation of damages and losses: winter wave in Colombia 2010-2011. Bogota.: Publicación de las Naciones Unidas. (reference in Spanish)
   Google Scholar
• Chacon J, Irigaray C, Fernandez T, EI, Hamdouni R. 2006. Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Environ. 65:341–411.
   Google Scholar
• Cooper MA, Addinson FT, Alvarez R, Coral M, Graham RH, Hayward SH, Taborda A. 1995. Basin development and tectonic history of the Llanos Basin, Eastern Cordillera, and Middle Magdalena Valley, Colombia. Am Assoc Pet Geol Bull. 79: 1421–1443.
   Google Scholar
• Costa JE, Baker VR. 1981. Surficial geology. Building with the earth. New York: Wiley.
   Google Scholar
• Cristina B, Nieves L, Samuel A. 2016. Statistical and spatial analysis of landside susceptibility maps with different classification systems. Enviro Earth Sci. 75: 1318.
   Google Scholar
• Cruden DM. 1991. A Simple definition of a landslide. Bull Int Assoc Eng Geol. 43: 27–29.
   Google Scholar
• Cruden DM, Varnes DJ. 1996. Landslides: investigation and mitigation, Chapter 3 “Landslide types and processes”. Washington D.C.: National Academy Press, Transportation Research Board Special Report 247.
   Google Scholar
• De Brio B, Sanz A. 2007. Redes neuronales y sistemas borrosos. México DF, México: Alfaomega.
   Google Scholar
• Ercanoglu M, Gokceoglu C. 2002. Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol. 41:720–730.
   Google Scholar
• ESRI.Environmental Systems Research Institute. [accessed 2017 January 1] Obtenido de http://desktop.arcgis.com/es/arcmap/latest/extensions/main/about-arcgis-for-desktop-extensions.htm
   Google Scholar
• Felicisimo AM. 1994. Digital terrain models. Oviedo: Pentalfa. Recovered on November 16, 2016, from http://www.etsimo.uniovi.es/∼feli. (reference in Spanish).
   Google Scholar
• Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage W. 2008. Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Eng Geol. 102:85–98.
   Google Scholar
• Gallant JC, Hutchinson MF. 2008. Digital terrain analysis. In McKenzie N J, Grundy MJ, Webster R, Ringrose-Voase AJ, editors. Guidelines for surveying soil and land resources. Australian Soil and Land Survey Handbook Series (Vol. 2). Melbourne: CSIRO.
   Google Scholar
• Gardiner CW. 1985. Handbook of stochastic methods (Vol. 3). Berlin: Springer.
   Google Scholar
• Gokceoglu C, Aksoy H. 1996. Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Eng Geol. 44:147–161.
   Google Scholar
• Hoek E, Brown ET. 1997. Practical estimates of rock mass strength. Int J Rock Mech Mining Sci. 34:1165–1186. V
   Google Scholar
• IDEAM. Institute of Hydrology, Meteorology and Environmental Studies of Colombia. [accessed 2016 December 28] Obtenido de http://www.ideam.gov.co/ (reference in Spanish).
   Google Scholar
• INGEOMINAS 2002. National catalog of landslide. Instituto de Investigacion e Informacion Geocientifica. Bogota: Instituto de Investigacion e Informacion Geocientifica (reference in Spanish).
   Google Scholar
• Julivert M, Tellez N. 1961. Geology of the W slope of the Eastern Cordillera in the sector of Bucaramanaga. Boletin De Geologia, Universidad Industrial De Santander; p. 39–42. reference in Spanish).
   Google Scholar
• Lee S. 2007. Landslide susceptibility mapping using an artificial neural network in the Gangneung area, Korea. Int J Remote Sens. 28:4763–4783.
   Google Scholar
• Lee S, Park I, Choi JK. 2012. Spatial prediction of ground subsidence susceptibility using an artificial neural network. Environ Manag. 49:347–358.
   Google Scholar
• Lee S, Ryu JH, Lee MJ, Won JS. 2003. Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environ Geol. 44:820–833.
   Google Scholar
• Lee S, Ryu JH, Won JS, Park HJ. 2004. Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol. 71:289–302.
   Google Scholar
• Leon LA. 1991. Geological map of the Department of Santander. Scale 1: 800.000. Boletín de Geología, Universidad Industrial de Santander; p. 53–63 (reference in Spanish).
   Google Scholar
• Martinez-Graña AM, Goy JL, Zazo C. 2016. Geomorphological applications for susceptibility mapping of landslides in natural parks. Environ Eng Manag J. 15:327.
   Google Scholar
• Martinez-Graña AM, Goy JL, De Bustamante I, Zazo C. 2014. Characterisation of environmental impact on resources, using strategic assessment of environmental impact and management of natural spaces of “Las Batuecas-Sierra de Francia” and “Quilamas” (Salamanca, Spain). Environ Earth Sci. 71:39–51.
   Google Scholar
• Martinez-Graña AM, Goy JL, Zazo C, Yenes M. 2013. Engineering geology maps for planning and management of natural parks: “Las Batuecas-Sierra de Francia” and “Quilamas” (Central Spanish System, Salamanca, Spain). Geosciences. 3:46–62.
   Google Scholar
• Matich DJ. 2001. Informática Aplicada a la Ingeniería de Procesos – Orientación I. En D. J. Matich, Neural Networks: Basic concepts and applications. Argentina: Universidad Tecnológica Nacional; p. 55 (reference in Spanish).
   Google Scholar
• Matula M. 1981. Rock and soil description and classification for engineering geological mapping report by the IAEG Commission on Engineering Geological Mapping. Netherlands: Bulletin of Engineering Geology and the Environment.
   Google Scholar
• Nefeslioglu HA, Sezer E, Gokceoglu C, Bozkir AS, Duman TY. 2010. Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Math Probl Eng. 2010:1–15.
   Google Scholar
• Ohlmacher GC. 2000. The relationship between geology and landslide hazards of Atchison, Kansas, and vicinity. Kansas: Kansas Geological Survey.
   Google Scholar
• Olaya V. 2009. Basic land-surface parameters. In: Hengl T, Reuter HI, editors. Geomorphometry: concepts, software, applications. Developments in Soil Science 33. Amsterdam: Elsevier; p. 141–169.
   Google Scholar
• Pike RJ. 1995. Geomorphometry: progress, pratice, and prospect. Z Geomorphol. 101:221–238.
   Google Scholar
• Pistocchi A, Luzi L, Napolitano P. 2002. The use of predictive modeling techniques for optimal exploitation of spatial databases: a case study in landslide hazard mapping with expert system-like methods. Environ Geol. 41:765.
   Google Scholar
• Poudyal CP, Chang C, Oh HJ, Lee S. 2010. Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environ Earth Sci. 61:1049–1064.
   Google Scholar
• Pradhan B, Lee S. 2010. 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.
   Google Scholar
• Pradhan B, Lee S, Buchroithner MF. 2010. A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Comput Environ Urban Syst. 34:216–235.
   Google Scholar
• Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A. 2014. Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci. 7:725–742.
   Google Scholar
• Saha AK, Gupta RP, Sarkar I, Arora MK, Csaplovics E. 2005. An approach for GIS-based statistical landslide susceptibility zonation— with a case study in the Himalayas. Landslides. 2:61–69.
   Google Scholar
• SIMMA. National System of Landslide “SGC”. [Accessed 2017 January 3] Obtenido de http://simma.sgc.gov.co/#/public/basic/ (reference in Spanish).
   Google Scholar
• Simpson PK. 1990. Artificial neural system—foundation, paradigm, application and implementation. New York: Pergamon Press.
   Google Scholar
• Soeters R, Van Westen CJ. 1996. Slope instability recognition, analysis and zonation. Washington, D.C.: National Academy Press.
   Google Scholar
• Suarez J. 2009. Slides: geotechnical analysis (Vol. I). Bucaramanga: Universidad Industrial de Santander. (reference in Spanish).
   Google Scholar
• Toro J. 1990. The termination of the Bucaramanga Fault in the Cordillera Oriental, Colombia, Master’s Thesis. Tucson: University of Arizona, Department of Geosciences.
   Google Scholar
• UNESCO. 1976. Engineering geological mapping. A guide to their preparation. Prepared by the Commission on Engineering Geological Maps of the International Association of Engineering Geology. Paris: Unesco Press.
   Google Scholar
• Valencia MA, Yañes C, Sanchez LP. 2006. Algoritmo Backpropagation para Redes Neuronales: conceptos y aplicaciones. Mexico: Instituto Politécnico Nacional Centro de Investigación en Computación.
   Google Scholar
• Van der Hammen T. 1958. Stratigraphy of Tertiary and Maestrichtian Continental and tectogenesis of the Colombian Andes. Boletín Geológico. Ingeominas Bogotá. 6:67–128. (reference in Spanish).
   Google Scholar
• Vargas RH, Arias AT, Jaramillo LC, Tellez NL. 1976. Geology of the plates 136 malaga and 152 soata, quadrangulo i-13, explanatory memory. instituto colombiano de geología y minería, ingeominas 80–93. (reference in Spanish).
   Google Scholar
• Yalcin A, Reis S, Aydinoglu A, Yomralioglu T. 2011. A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena. 85:274–287.
   Google Scholar