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Journal ArticleDOI

Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment

C.J. van Westen, +2 more
- 01 Nov 2003 - 
- Vol. 30, Iss: 3, pp 399-419
TLDR
In this article, the importance of geomorphological expert knowledge in the generation of landslide susceptibility maps, using GIS supported indirect bivariate statistical analysis, was evaluated using a test area in the Alpago region in Italy, where a dataset was generated at scale 1:5,000.
Abstract
The objective of this paper is to evaluate the importance of geomorphological expert knowledge in the generation of landslide susceptibility maps, using GIS supported indirect bivariate statistical analysis. For a test area in the Alpago region in Italy a dataset was generated at scale 1:5,000. Detailed geomorphological maps were generated, with legends at different levels of complexity. Other factor maps, that were considered relevant for the assessment of landslide susceptibility, were also collected, such as lithology, structural geology, surficial materials, slope classes, land use, distance from streams, roads and houses. The weights of evidence method was used to generate statistically derived weights for all classes of the factor maps. On the basis of these weights, the most relevant maps were selected for the combination into landslide susceptibility maps. Six different combinations of factor maps were evaluated, with varying geomorphological input. Success rates were used to classify the weight maps into three qualitative landslide susceptibility classes. The resulting six maps were compared with a direct susceptibility map, which was made by direct assignment of susceptibility classes in the field. The analysis indicated that the use of detailed geomorphological information in the bivariate statistical analysis raised the overall accuracy of the final susceptibility map considerably. However, even with the use of a detailed geomorphological factor map, the difference with the separately prepared direct susceptibility map is still significant, due to the generalisations that are inherent to the bivariate statistical analysis technique.

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Journal ArticleDOI

Landslide hazard and risk zonation—why is it still so difficult?

TL;DR: In this paper, the authors review the problem of attempting to quantify landslide risk over larger areas, discussing a number of difficulties related to the generation of landslide inventory maps including information on date, type and volume of the landslide, the determination of its spatial and temporal probability, the modelling of runout and the assessment of landslide vulnerability.
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Spatial prediction models for landslide hazards: review, comparison and evaluation

TL;DR: In a case study from the Ecuadorian Andes, logistic regression with stepwise backward variable selection yields lowest error rates and demonstrates the best generalization capabilities.
Journal ArticleDOI

Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling

TL;DR: A comparison of traditional statistical and novel machine learning models applied for regional scale landslide susceptibility modeling is presented and it is suggested that the framework of this model evaluation approach can be applied to assist in selection of a suitable landslide susceptibility modeled technique.
Journal ArticleDOI

GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China

TL;DR: A detailed landslide susceptibility map was produced using a logistic regression method with datasets developed for a geographic information system (GIS), known as one of the most landslide-prone areas in China, the Zhongxian-Shizhu segment in the Three Gorges Reservoir region of China was selected as a suitable case to evaluate the frequency and distribution of landslides as mentioned in this paper.
References
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Book

Geographic Information Systems for Geoscientists: Modelling with GIS

TL;DR: An introduction to GIS and tools for map analysis: map pairs, spatial data models, and more.
Book

Landslide hazard zonation: A review of principles and practice

TL;DR: In this paper, the authors give the definitions and principles of landslides, and identify causative conditions and processes (inherent or basic conditions, geology, geomorphology, hydrologic conditions and climate, vegetation, factors that change stress conditions and strength of materials).
Journal ArticleDOI

Landslide hazard assessment: summary review and new perspectives

TL;DR: In this paper, the authors present a summary review and a classification of the main approaches that have been developed world-wide for the assessment of hazard and risk of landsliding, and several considerations concerning acceptable risk and risk management are presented.
Journal Article

Probabilistic prediction models for landslide hazard mapping

TL;DR: In this article, a joint conditional probability model is proposed to represent a measure of a future landslide hazard, and five estimation procedures for the model are presented, where the distribution of past landslides was divided into two groups with respect to a fixed time.
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