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Environmental Earth Sciences
Published in: Environmental Earth Sciences 4/2017

01-02-2017 | Original Article

Comparing data-driven landslide susceptibility models based on participatory landslide inventory mapping in Purwosari area, Yogyakarta, Java

Authors: Guruh Samodra, Guangqi Chen, Junun Sartohadi, Kiyonobu Kasama

Published in: Environmental Earth Sciences | Issue 4/2017

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Abstract

There are different approaches and techniques for landslide susceptibility mapping. However, no agreement has been reached in both the procedure and the use of specific controlling factors employed in the landslide susceptibility mapping. Each model has its own assumption, and the result may differ from place to place. Different landslide controlling factors and the completeness of landslide inventory may also affect the different result. Incomplete landslide inventory may produce significance error in the interpretation of the relationship between landslide and controlling factor. Comparing landslide susceptibility models using complete inventory is essential in order to identify the most realistic landslide susceptibility approach applied typically in the tropical region Indonesia. Purwosari area, Java, which has total 182 landslides occurred from 1979 to 2011, was selected as study area to evaluate three data-driven landslide susceptibility models, i.e., weight of evidence, logistic regression, and artificial neural network. Landslide in the study area is usually affected by rainfall and anthropogenic activities. The landslide typology consists of shallow translational and rotational slide. The elevation, slope, aspect, plan curvature, profile curvature, stream power index, topographic wetness index, distance to river, land use, and distance to road were selected as landslide controlling factors for the analysis. Considering the accuracy and the precision evaluations, the weight of evidence represents considerably the most realistic prediction capacities (79%) when comparing with the logistic regression (72%) and artificial neural network (71%). The linear model shows more powerful result than the nonlinear models because it fits to the area where complete landslide inventory is available, the landscape is not varied, and the occurence of landslide is evenly distributed to the class of controlling factor.
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Metadata
Title
Comparing data-driven landslide susceptibility models based on participatory landslide inventory mapping in Purwosari area, Yogyakarta, Java
Authors
Guruh Samodra
Guangqi Chen
Junun Sartohadi
Kiyonobu Kasama
Publication date
01-02-2017
Publisher
Springer Berlin Heidelberg
Published in
Environmental Earth Sciences / Issue 4/2017
Print ISSN: 1866-6280
Electronic ISSN: 1866-6299
DOI
https://doi.org/10.1007/s12665-017-6475-2

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