Journal
REMOTE SENSING
Volume 10, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/rs10101545
Keywords
landslide susceptibility; decision tree; CHAID; exhaustive CHAID; QUEST
Categories
Funding
- Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) - Ministry of Science and ICT
- National Research Foundation of Korea (NRF) - Korea government (MSIP) [2017R1A2B4003258, NRF-2018R1D1A1B07041203]
- National Research Council of Science & Technology (NST), Republic of Korea [18-3214] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2017R1A2B4003258] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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.
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