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Proceedings Paper

A GIS-based landslide hazard assessment by multiple regression analysis
Author(s): Xiaoduo Pan; Hiroyuki Nakamura; Nozaki Tamotsu; Zhuotong Nan
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Paper Abstract

The occurrence of landslides generally depends on complex interactions among a large number of partially interrelated factors. It is appropriate to use multiple regression analysis for predicting landslides from a given set of independent variables. The procedure of landslide hazard assessment by regression analysis, however, requires evaluation of the spatially varying terrain conditions as well as spatial representation of the landslides. In this paper, the multiple regression analysis was applied to predict landslides in Himi district from independent factors, such as geology, slope-aspect, slope angle, land use and soil with Geographic Information System (GIS). Based on GIS, every factor was classified into several clusters and then the statistical weight of every cluster was assigned for every factor respectively. By the weights of five factors, the linear regression's coefficients of these input factors in landslide area were extracted and assigned to the whole region, and then the susceptibility for the potential landslide was obtained to make the landslide hazard assessment map. Geology and slope-aspect factors are the most important ones. Soil factor is not so notable in this research region, though it may be significant in other regions. At last, the average susceptibilities map for existing landslides was made for the engineers to do control work.

Paper Details

Date Published: 7 August 2007
PDF: 13 pages
Proc. SPIE 6754, Geoinformatics 2007: Geospatial Information Technology and Applications, 67541M (7 August 2007); doi: 10.1117/12.764899
Show Author Affiliations
Xiaoduo Pan, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Hiroyuki Nakamura, Tokyo Univ. of Agriculture and Technology (Japan)
Nozaki Tamotsu, CNK Geotechnical Institute Inc. (Japan)
Zhuotong Nan, Cold and Arid Regions Environmental and Engineering Research Institute (China)


Published in SPIE Proceedings Vol. 6754:
Geoinformatics 2007: Geospatial Information Technology and Applications

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