A GIS-based landslide hazard assessment by multiple regression analysis

Xiaoduo Pan; Hiroyuki Nakamura; Nozaki Tamotsu; Zhuotong Nan

doi:10.1117/12.764899

7 August 2007 A GIS-based landslide hazard assessment by multiple regression analysis

Xiaoduo Pan, Hiroyuki Nakamura, Nozaki Tamotsu, Zhuotong Nan

Proceedings Volume 6754, Geoinformatics 2007: Geospatial Information Technology and Applications; 67541M (2007) https://doi.org/10.1117/12.764899
Event: Geoinformatics 2007, 2007, Nanjing, China

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.

Citation Download Citation

Xiaoduo Pan, Hiroyuki Nakamura, Nozaki Tamotsu, and Zhuotong Nan "A GIS-based landslide hazard assessment by multiple regression analysis", Proc. SPIE 6754, Geoinformatics 2007: Geospatial Information Technology and Applications, 67541M (7 August 2007); https://doi.org/10.1117/12.764899

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
13 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Landslide (networking)

Geology

Geographic information systems

Hazard analysis

Statistical analysis

Soil science

Analytical research

Show All Keywords

Keywords/Phrases

Search In:

Publication Years