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ISPRS Int. J. Geo-Inf. 2017, 6(1), 18; doi:10.3390/ijgi6010018

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

1
School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
School of Environment and Resource, Zhejiang Agriculture and Forestry University, Hangzhou 311300, China
*
Author to whom correspondence should be addressed.
Academic Editors: Jason K. Levy and Wolfgang Kainz
Received: 28 September 2016 / Revised: 15 December 2016 / Accepted: 6 January 2017 / Published: 16 January 2017
View Full-Text   |   Download PDF [4598 KB, uploaded 16 January 2017]   |  

Abstract

Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM), an information value model improved by an analytic hierarchy process (IVM-AHP) and our new improved model. Approximately 70% (5905) of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530) were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance. View Full-Text
Keywords: landslide; susceptibility assessment; GIS; improved information value model; Chongqing; gray clustering landslide; susceptibility assessment; GIS; improved information value model; Chongqing; gray clustering
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Ba, Q.; Chen, Y.; Deng, S.; Wu, Q.; Yang, J.; Zhang, J. An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping. ISPRS Int. J. Geo-Inf. 2017, 6, 18.

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