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Open AccessArticle

Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea

1
Division of Science Education, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si, Gangwon-do 24341, Korea
2
Geological Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), Gajeong-dong 30, Yuseong-gu, Daejeon 305-350, Korea
3
Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Korea
4
Center for Environmental Assessment Monitoring, Environmental Assessment Group, Korea Environment Institute, 370 Sicheong-daero, Sejong-si 399-007, Korea
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1545; https://doi.org/10.3390/rs10101545
Received: 21 August 2018 / Revised: 19 September 2018 / Accepted: 21 September 2018 / Published: 25 September 2018
We assessed landslide susceptibility using Chi-square Automatic Interaction Detection (CHAID), exhaustive CHAID, and Quick, Unbiased, and Efficient Statistical Tree (QUEST) decision tree models in Jumunjin-eup, Gangneung-si, Korea. A total of 548 landslides were identified based on interpretation of aerial photographs. Half of the 548 landslides were selected for modeling, and the remaining half were used for verification. We used 20 landslide control factors that were classified into five categories, namely topographic elements, hydrological elements, soil maps, forest maps, and geological maps, to determine landslide susceptibility. The relationships of landslide occurrence with landslide-inducing factors were analyzed using CHAID, exhaustive CHAID, and QUEST models. The three models were then verified using the area under the curve (AUC) method. The results showed that the CHAID model (AUC = 87.1%) was more accurate than the exhaustive CHAID (AUC = 86.9%) and QUEST models (AUC = 82.8%). The verification results showed that the CHAID model had the highest accuracy. There was high susceptibility to landslides in mountainous areas and low susceptibility in coastal areas. Analyzing the characteristics of the landslide control factors in advance will enable us to obtain more accurate results. View Full-Text
Keywords: landslide susceptibility; decision tree; CHAID; exhaustive CHAID; QUEST landslide susceptibility; decision tree; CHAID; exhaustive CHAID; QUEST
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MDPI and ACS Style

Park, S.-J.; Lee, C.-W.; Lee, S.; Lee, M.-J. Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea. Remote Sens. 2018, 10, 1545.

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