Shallow landslide susceptibility : modelling and validation
(2010) In LUMA-GIS Thesis GISM01 20091Dept of Physical Geography and Ecosystem Science
- Abstract
- Rainfall frequently triggers shallow landslides in mountainous areas
worldwide. Landslide susceptibility maps express the probability of
occurrence of landslides based on terrain conditions; they are useful for
disaster prevention and land use planning. This report is about val-
idating a qualitative approach to map global landslide susceptibility,
based on the weighted linear combination (WLC) of slope gradient,
soil type, soil texture, elevation, land cover and drainage density. The
parameters are derived from digital global databases. The accuracy
assessment was based on a detailed landslide inventory of a 160-km2
area in Japan, using the receiver-operating characteristic (ROC) plot
area under the curve (AUC). The AUC... (More) - Rainfall frequently triggers shallow landslides in mountainous areas
worldwide. Landslide susceptibility maps express the probability of
occurrence of landslides based on terrain conditions; they are useful for
disaster prevention and land use planning. This report is about val-
idating a qualitative approach to map global landslide susceptibility,
based on the weighted linear combination (WLC) of slope gradient,
soil type, soil texture, elevation, land cover and drainage density. The
parameters are derived from digital global databases. The accuracy
assessment was based on a detailed landslide inventory of a 160-km2
area in Japan, using the receiver-operating characteristic (ROC) plot
area under the curve (AUC). The AUC permitted to compare analysis
approaches and dierent parameter combinations. The AUC for the
WLC model was 0.47, below a random classication. Two approaches
improved the model accuracy, using the weights of evidence (WOE)
approach raised the accuracy to 0.64, and using a higher resolution
DEM raised the accuracy to 0.66. On the other hand, a quantitat-
ive approach based on logistic regression (LR) and using the software
package Spatial Data Modeller (SDM) produced models with AUC
between 0.67 and 0.71. The highest accuracy for a model including
lithology, slope gradient, prole curvature, plan curvature and elev-
ation. The reason for the higher accuracy of the LR models is that
the occurrence of landslides depends on local conditions, expressed by
the quantitative relations, while the qualitative weights of the WLC
model were developed for a global model using different criteria. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/3559066
- author
- Pimiento Chamorro, Edgar LU
- supervisor
- organization
- course
- GISM01 20091
- year
- 2010
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- landslide susceptibility, weights of evidence, logistic regression, ROC plot AUC, validation
- publication/series
- LUMA-GIS Thesis
- report number
- 6
- language
- English
- additional info
- Hiromitsu Yamagishi, Ehime University, Japan.
- id
- 3559066
- date added to LUP
- 2013-02-28 11:31:59
- date last changed
- 2013-02-28 14:45:17
@misc{3559066, abstract = {{Rainfall frequently triggers shallow landslides in mountainous areas worldwide. Landslide susceptibility maps express the probability of occurrence of landslides based on terrain conditions; they are useful for disaster prevention and land use planning. This report is about val- idating a qualitative approach to map global landslide susceptibility, based on the weighted linear combination (WLC) of slope gradient, soil type, soil texture, elevation, land cover and drainage density. The parameters are derived from digital global databases. The accuracy assessment was based on a detailed landslide inventory of a 160-km2 area in Japan, using the receiver-operating characteristic (ROC) plot area under the curve (AUC). The AUC permitted to compare analysis approaches and dierent parameter combinations. The AUC for the WLC model was 0.47, below a random classication. Two approaches improved the model accuracy, using the weights of evidence (WOE) approach raised the accuracy to 0.64, and using a higher resolution DEM raised the accuracy to 0.66. On the other hand, a quantitat- ive approach based on logistic regression (LR) and using the software package Spatial Data Modeller (SDM) produced models with AUC between 0.67 and 0.71. The highest accuracy for a model including lithology, slope gradient, prole curvature, plan curvature and elev- ation. The reason for the higher accuracy of the LR models is that the occurrence of landslides depends on local conditions, expressed by the quantitative relations, while the qualitative weights of the WLC model were developed for a global model using different criteria.}}, author = {{Pimiento Chamorro, Edgar}}, language = {{eng}}, note = {{Student Paper}}, series = {{LUMA-GIS Thesis}}, title = {{Shallow landslide susceptibility : modelling and validation}}, year = {{2010}}, }