Assessment of susceptibility to landslides in the eastern cordillera of Colombia using the weight of evidence statistical method
Analyses of susceptible areas to landslides in mountain regions are a fundamental approach to identify areas prone to suffer future landslides, in order to prevent any natural disaster and associated life and infrastructure losses. In this project a statistical approach based on the Bayesian theorem...
- Autores:
-
Calderón Guevara, Wilmar Andrés
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2020
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/48951
- Acceso en línea:
- http://hdl.handle.net/1992/48951
- Palabra clave:
- Desprendimientos de tierra
Prevención de desastres
Evaluación de riesgos
Desastres naturales
Sistemas de información geográfica
Geociencias
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-sa/4.0/
Summary: | Analyses of susceptible areas to landslides in mountain regions are a fundamental approach to identify areas prone to suffer future landslides, in order to prevent any natural disaster and associated life and infrastructure losses. In this project a statistical approach based on the Bayesian theorem was used to assess the susceptibility to landslides in an area on the eastern margin of the Eastern Cordillera of Colombia. Bayes theorem was employed by using the Weight Of Evidence (WOE) method to produce a susceptibility map of the study area. The fundamental assumption of the method is that future landslides will occur under circumstances similar to previous landslides. Modelling of landslide susceptibility was carried out by using geographical information systems (GIS) analysis and data integration. A series of 14 causative factors were analyzed to determine their relative contribution to landslide occurrence. The final susceptibility map is based on independent causative factors obtained through a X² non-parametric test. The algorithm used in this project accomplished model accuracies of 80.88% for the study area and 91.87% for a smaller area inside the original study area, which demonstrate its applicability |
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