Ensemble-based landslide susceptibility maps in Jinbu area, Korea

Moung Jin Lee, Jae Won Choi, Hyun Joo Oh, Joong-sun Won, Inhye Park, Saro Lee

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

Ensemble techniques were developed, applied and validated for the analysis of landslide susceptibility in Jinbu area, Korea using the geographic information system (GIS). Landslide-occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 70/30 ratio for training and validation of the models, respectively. Topography, geology, soil and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The relationships between the detected landslide locations and the factors were identified and quantified by frequency ratio, weight of evidence, logistic regression and artificial neural network models and their ensemble models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. Then, the four landslide susceptibility maps were used as new input factors and integrated using the frequency ratio, weight of evidence, logistic regression and artificial neural network models as ensemble methods to make better susceptibility maps. All of the susceptibility maps were validated by comparison with known landslide locations that were not used directly in the analysis. As the result, the ensemble-based landslide susceptibility map that used the new landslide-related input factor maps showed better accuracy (87. 11% in frequency ratio, 83. 14% in weight of evidence, 87. 79% in logistic regression and 84. 54% in artificial neural network) than the individual landslide susceptibility maps (84. 94% in frequency ratio, 82. 82% in weight of evidence, 87. 72% in logistic regression and 81. 44% in artificial neural network). All accuracy assessments showed overall satisfactory agreement of more than 80%. The ensemble model was found to be more effective in terms of prediction accuracy than the individual model.

Original languageEnglish
Pages (from-to)23-37
Number of pages15
JournalEnvironmental Earth Sciences
Volume67
Issue number1
DOIs
Publication statusPublished - 2012 Sep 1

Fingerprint

landslides
Landslides
Korean Peninsula
landslide
weight-of-evidence
artificial neural network
neural networks
Logistics
logistics
Neural networks
accuracy assessment
model validation
geology
Geology
aerial photograph
geographic information systems
Geographic information systems
Topography
photographs
field survey

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

Lee, Moung Jin ; Choi, Jae Won ; Oh, Hyun Joo ; Won, Joong-sun ; Park, Inhye ; Lee, Saro. / Ensemble-based landslide susceptibility maps in Jinbu area, Korea. In: Environmental Earth Sciences. 2012 ; Vol. 67, No. 1. pp. 23-37.
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abstract = "Ensemble techniques were developed, applied and validated for the analysis of landslide susceptibility in Jinbu area, Korea using the geographic information system (GIS). Landslide-occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 70/30 ratio for training and validation of the models, respectively. Topography, geology, soil and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The relationships between the detected landslide locations and the factors were identified and quantified by frequency ratio, weight of evidence, logistic regression and artificial neural network models and their ensemble models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. Then, the four landslide susceptibility maps were used as new input factors and integrated using the frequency ratio, weight of evidence, logistic regression and artificial neural network models as ensemble methods to make better susceptibility maps. All of the susceptibility maps were validated by comparison with known landslide locations that were not used directly in the analysis. As the result, the ensemble-based landslide susceptibility map that used the new landslide-related input factor maps showed better accuracy (87. 11{\%} in frequency ratio, 83. 14{\%} in weight of evidence, 87. 79{\%} in logistic regression and 84. 54{\%} in artificial neural network) than the individual landslide susceptibility maps (84. 94{\%} in frequency ratio, 82. 82{\%} in weight of evidence, 87. 72{\%} in logistic regression and 81. 44{\%} in artificial neural network). All accuracy assessments showed overall satisfactory agreement of more than 80{\%}. The ensemble model was found to be more effective in terms of prediction accuracy than the individual model.",
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Ensemble-based landslide susceptibility maps in Jinbu area, Korea. / Lee, Moung Jin; Choi, Jae Won; Oh, Hyun Joo; Won, Joong-sun; Park, Inhye; Lee, Saro.

In: Environmental Earth Sciences, Vol. 67, No. 1, 01.09.2012, p. 23-37.

Research output: Contribution to journalArticle

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