Validation of an artificial neural network model for landslide susceptibility mapping

Jaewon Choi, Hyun Joo Oh, Joong Sun Won, Saro Lee

Research output: Contribution to journalArticle

57 Citations (Scopus)

Abstract

The aim of this study was to validate an artificial neural network model at Youngin, Janghung, and Boeun, Korea, using the geographic information system (GIS). The factors that influence landslide occurrence, such as the slope, aspect, curvature, and geomorphology of topography, the type, material, drainage, and effective thickness of soil, the type, diameter, age, and density of forest, distance from lineament, and land cover were either calculated or extracted from the spatial database and Landsat TM satellite images. Landslide susceptibility was analyzed using the landslide occurrence factors provided by the artificial neural network model. The landslide susceptibility analysis results were validated and cross-validated using the landslide locations as study areas. For this purpose, weights for each study area were calculated by the artificial neural network model. Among the nine cases, the best accuracy (81.36%) was obtained in the case of the Boeun-based Janghung weight, whereas the Janghung-based Youngin weight showed the worst accuracy (71.72%).

Original languageEnglish
Pages (from-to)473-483
Number of pages11
JournalEnvironmental Earth Sciences
Volume60
Issue number3
DOIs
Publication statusPublished - 2010 Apr 1

Fingerprint

landslides
Landslides
artificial neural network
neural networks
landslide
Neural networks
Geomorphology
geomorphology
Landsat
lineament
land cover
Landsat thematic mapper
geographic information systems
Geographic information systems
Topography
Drainage
curvature
Korean Peninsula
topography
soil types

All Science Journal Classification (ASJC) codes

  • Global and Planetary Change
  • Environmental Chemistry
  • Water Science and Technology
  • Soil Science
  • Pollution
  • Geology
  • Earth-Surface Processes

Cite this

@article{bfa7611eaac7418788a3e5041f2be960,
title = "Validation of an artificial neural network model for landslide susceptibility mapping",
abstract = "The aim of this study was to validate an artificial neural network model at Youngin, Janghung, and Boeun, Korea, using the geographic information system (GIS). The factors that influence landslide occurrence, such as the slope, aspect, curvature, and geomorphology of topography, the type, material, drainage, and effective thickness of soil, the type, diameter, age, and density of forest, distance from lineament, and land cover were either calculated or extracted from the spatial database and Landsat TM satellite images. Landslide susceptibility was analyzed using the landslide occurrence factors provided by the artificial neural network model. The landslide susceptibility analysis results were validated and cross-validated using the landslide locations as study areas. For this purpose, weights for each study area were calculated by the artificial neural network model. Among the nine cases, the best accuracy (81.36{\%}) was obtained in the case of the Boeun-based Janghung weight, whereas the Janghung-based Youngin weight showed the worst accuracy (71.72{\%}).",
author = "Jaewon Choi and Oh, {Hyun Joo} and Won, {Joong Sun} and Saro Lee",
year = "2010",
month = "4",
day = "1",
doi = "10.1007/s12665-009-0188-0",
language = "English",
volume = "60",
pages = "473--483",
journal = "Environmental Earth Sciences",
issn = "1866-6280",
publisher = "Springer Verlag",
number = "3",

}

Validation of an artificial neural network model for landslide susceptibility mapping. / Choi, Jaewon; Oh, Hyun Joo; Won, Joong Sun; Lee, Saro.

In: Environmental Earth Sciences, Vol. 60, No. 3, 01.04.2010, p. 473-483.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Validation of an artificial neural network model for landslide susceptibility mapping

AU - Choi, Jaewon

AU - Oh, Hyun Joo

AU - Won, Joong Sun

AU - Lee, Saro

PY - 2010/4/1

Y1 - 2010/4/1

N2 - The aim of this study was to validate an artificial neural network model at Youngin, Janghung, and Boeun, Korea, using the geographic information system (GIS). The factors that influence landslide occurrence, such as the slope, aspect, curvature, and geomorphology of topography, the type, material, drainage, and effective thickness of soil, the type, diameter, age, and density of forest, distance from lineament, and land cover were either calculated or extracted from the spatial database and Landsat TM satellite images. Landslide susceptibility was analyzed using the landslide occurrence factors provided by the artificial neural network model. The landslide susceptibility analysis results were validated and cross-validated using the landslide locations as study areas. For this purpose, weights for each study area were calculated by the artificial neural network model. Among the nine cases, the best accuracy (81.36%) was obtained in the case of the Boeun-based Janghung weight, whereas the Janghung-based Youngin weight showed the worst accuracy (71.72%).

AB - The aim of this study was to validate an artificial neural network model at Youngin, Janghung, and Boeun, Korea, using the geographic information system (GIS). The factors that influence landslide occurrence, such as the slope, aspect, curvature, and geomorphology of topography, the type, material, drainage, and effective thickness of soil, the type, diameter, age, and density of forest, distance from lineament, and land cover were either calculated or extracted from the spatial database and Landsat TM satellite images. Landslide susceptibility was analyzed using the landslide occurrence factors provided by the artificial neural network model. The landslide susceptibility analysis results were validated and cross-validated using the landslide locations as study areas. For this purpose, weights for each study area were calculated by the artificial neural network model. Among the nine cases, the best accuracy (81.36%) was obtained in the case of the Boeun-based Janghung weight, whereas the Janghung-based Youngin weight showed the worst accuracy (71.72%).

UR - http://www.scopus.com/inward/record.url?scp=77954151133&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77954151133&partnerID=8YFLogxK

U2 - 10.1007/s12665-009-0188-0

DO - 10.1007/s12665-009-0188-0

M3 - Article

AN - SCOPUS:77954151133

VL - 60

SP - 473

EP - 483

JO - Environmental Earth Sciences

JF - Environmental Earth Sciences

SN - 1866-6280

IS - 3

ER -