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Received: 2006-03-20

Revision Accepted: 2006-09-21

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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.12 P.2007-2017


GIS-based logistic regression method for landslide susceptibility mapping in regional scale

Author(s):  ZHU Lei, HUANG Jing-feng

Affiliation(s):  Department of Natural Science, Zhejiang University, Hangzhou 310029, China; more

Corresponding email(s):   hjf@zju.edu.cn

Key Words:  Landslide, Susceptibility, Logistic regression, GIS, Spatial analysis

ZHU Lei, HUANG Jing-feng. GIS-based logistic regression method for landslide susceptibility mapping in regional scale[J]. Journal of Zhejiang University Science A, 2006, 7(12): 2007-2017.

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A1 - HUANG Jing-feng
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landslide susceptibility map is one of the study fields portraying the spatial distribution of future slope failure susceptibility. This paper deals with past methods for producing landslide susceptibility map and divides these methods into 3 types. The loGIStic linear regression approach is further elaborated on by crosstabs method, which is used to analyze the relationship between the categorical or binary response variable and one or more continuous or categorical or binary explanatory variables derived from samples. It is an objective assignment of coefficients serving as weights of various factors under considerations while expert opinions make great difference in heuristic approaches. Different from deterministic approach, it is very applicable to regional scale. In this study, double GIStic regression%29&ck%5B%5D=abstract&ck%5B%5D=keyword'>loGIStic regression is applied in the study area. The entire study area is first analyzed. The GIStic regression%29&ck%5B%5D=abstract&ck%5B%5D=keyword'>loGIStic regression equation showed that elevation, proximity to road, river and residential area are main factors triggering landslide occurrence in this area. The prediction accuracy of the first landslide susceptibility map was showed to be 80%. Along the road and residential area, almost all areas are in high landslide susceptibility zone. Some non-landslide areas are incorrectly divided into high and medium landslide susceptibility zone. In order to improve the status, a second GIStic regression%29&ck%5B%5D=abstract&ck%5B%5D=keyword'>loGIStic regression was done in high landslide susceptibility zone using landslide cells and non-landslide sample cells in this area. In the second GIStic regression%29&ck%5B%5D=abstract&ck%5B%5D=keyword'>loGIStic regression analysis, only engineering and geological conditions are important in these areas and are entered in the new GIStic regression%29&ck%5B%5D=abstract&ck%5B%5D=keyword'>loGIStic regression equation indicating that only areas with unstable engineering and geological conditions are prone to landslide during large scale engineering activity. Taking these two GIStic regression%29&ck%5B%5D=abstract&ck%5B%5D=keyword'>loGIStic regression results into account yields a new landslide susceptibility map. Double GIStic regression%29&ck%5B%5D=abstract&ck%5B%5D=keyword'>loGIStic regression analysis improved the non-landslide prediction accuracy. During calculation of parameters for GIStic regression%29&ck%5B%5D=abstract&ck%5B%5D=keyword'>loGIStic regression, landslide density is used to transform nominal variable to numeric variable and this avoids the creation of an excessively high number of dummy variables.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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