Full Text:   <1928>

CLC number: P642.2

On-line Access: 

Received: 2006-03-20

Revision Accepted: 2006-09-21

Crosschecked: 0000-00-00

Cited: 11

Clicked: 3323

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

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.

@article{title="GIS-based logistic regression method for landslide susceptibility mapping in regional scale",
author="ZHU Lei, HUANG Jing-feng",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T GIS-based logistic regression method for landslide susceptibility mapping in regional scale
%A ZHU Lei
%A HUANG Jing-feng
%J Journal of Zhejiang University SCIENCE A
%V 7
%N 12
%P 2007~2017
%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A2007

T1 - GIS-based logistic regression method for landslide susceptibility mapping in regional scale
A1 - ZHU Lei
A1 - HUANG Jing-feng
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 12
SP - 2007
EP - 2017
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.A2007

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


[1] Aleotti, P., Chowdhury, R., 1999. Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and the Environment, 58(1):21-44.

[2] Atkinson, P.M., Massari, R., 1998. Generalized linear modelling of landslide susceptibility in the Central Apennines. Computers and Geosciences, 24(4):373-385.

[3] Ayalew, L., Yamagishi, H., 2004. Slope movements in the Blue Nile basin, as seen from landscape evolution perspective. Geomorphology, 57(1-2):95-116.

[4] Ayalew, L., Yamagishi, H., 2005. The application of GIS-based logistic regression for susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65(1-2):15-31.

[5] Ayalew, L., Yamagishi, H., Ugawa, N., 2004. Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides, 1(1):73-81.

[6] Barredo, J.I., Benavidesz, A., Herhl, J., van Westen, C.J., 2000. Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain. International Journal of Applied Earth Observation and Geoinformation, 2(1):9-23.

[7] Beguería, S., 2006. Changes in land cover and shallow landslide activity: A case study in the Spanish Pyrenees. Geomorphology, 74(1-4):196-206.

[8] Carrara, A., 1983. Uncertainty in Evaluating Landslide Hazard and Risk. In: Nemec, J., Nigs, J.M., Siccardi, F. (Eds.), Prediction and Perception of Natural Hazards. Kluwer, Dordrecht, The Netherlands, p.101-111.

[9] Clerici, A., Perego, S., Tellini, C., Vescovi, P., 2002. A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology, 48(4):349-364.

[10] Dai, F.C., Lee, C.F., 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, 42(3-4):213-228.

[11] Donati, L., Turrini, M.C., 2002. An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines (Valnerina; Perugia, Italy). Engineering Geology, 63(3-4):277-289.

[12] Ermini, L., Catani, F., Casagli, N., 2005. Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology, 66(1-4):327-343.

[13] Gómez, H., Kavzoglu, T., 2005. Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Engineering Geology, 78(1-2):11-27.

[14] Gray, D.H., Leiser, A.T., 1982. Biotechnical Slope Protection and Erosion Control. Van Nostrand-Reinhold, New York, p.271.

[15] Greenway, D.R., 1987. Vegetation and Slope Stability. In: Anderson, M.G., Richards, K.S. (Eds.), Slope Stability. Wiley, New York, p.187-230.

[16] Gritzner, M.L., Marcus, W.A., Aspinall, R., Custer, S.G., 2001. Assessing landslide potential using GIS, soil wetness modeling and topographic attributes, Payette River, Idaho. Geomorphology, 37(1-2):149-165.

[17] Guzzetti, F., Carrara, A., Cardinali, M., Reichenbach, P., 1999. Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, central Italy. Geomorphology, 31(1-4):181-216.

[18] Lee, S., Ryu, J., Min, K., Won, J., 2001. Development of Two Artificial Neural Network Methods for Landslide Susceptibility Analysis. Proceedings of the Geoscience and Remote Sensing Symposium, IGARSS’01, IEEE 2001 International, 5:2364-2366.

[19] Menard, S.W., 1995. Applied Logistic Regression Analysis. SAGE Publication, Inc., Thousand Oaks, CA.

[20] Ohlmacher, G.C., Davis, J.C., 2003. Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Engineering Geology, 69(3-4):331-343.

[21] Saaty, T.L., 1980. The Analytical Hierarchy Process. McGraw Hill, New York, p.350.

[22] Tasser, E., Mader, M., Tappeiner, U., 2003. Effects of land use in alpine grasslands on the probability of landslides. Basic Appl. Ecol., 4(3):271-280.

[23] Yesilnacar, E., Topal, T., 2005. Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology, 79(3-4):251-266.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE