Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea
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...
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doaj-281307618f7049f681dafa748a9b8c732020-11-25T00:57:50ZengMDPI AGRemote Sensing2072-42922018-09-011010154510.3390/rs10101545rs10101545Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, KoreaSung-Jae Park0Chang-Wook Lee1Saro Lee2Moung-Jin Lee3Division of Science Education, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si, Gangwon-do 24341, KoreaDivision of Science Education, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si, Gangwon-do 24341, KoreaGeological Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), Gajeong-dong 30, Yuseong-gu, Daejeon 305-350, KoreaCenter for Environmental Assessment Monitoring, Environmental Assessment Group, Korea Environment Institute, 370 Sicheong-daero, Sejong-si 399-007, KoreaWe 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.http://www.mdpi.com/2072-4292/10/10/1545landslide susceptibilitydecision treeCHAIDexhaustive CHAIDQUEST |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sung-Jae Park Chang-Wook Lee Saro Lee Moung-Jin Lee |
spellingShingle |
Sung-Jae Park Chang-Wook Lee Saro Lee Moung-Jin Lee Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea Remote Sensing landslide susceptibility decision tree CHAID exhaustive CHAID QUEST |
author_facet |
Sung-Jae Park Chang-Wook Lee Saro Lee Moung-Jin Lee |
author_sort |
Sung-Jae Park |
title |
Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea |
title_short |
Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea |
title_full |
Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea |
title_fullStr |
Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea |
title_full_unstemmed |
Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea |
title_sort |
landslide susceptibility mapping and comparison using decision tree models: a case study of jumunjin area, korea |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2018-09-01 |
description |
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. |
topic |
landslide susceptibility decision tree CHAID exhaustive CHAID QUEST |
url |
http://www.mdpi.com/2072-4292/10/10/1545 |
work_keys_str_mv |
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