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Open AccessArticle

Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping

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), 124 Gwahang-no, Yuseong-gu, Daejeon 305-350, Korea
3
Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-roYuseong-gu, Daejeon 305-350, Korea
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(8), 1252; https://doi.org/10.3390/rs10081252
Received: 21 June 2018 / Revised: 3 August 2018 / Accepted: 5 August 2018 / Published: 9 August 2018
The main purpose of this study was to produce landslide susceptibility maps using various ensemble-based machine learning models (i.e., the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models) for the Sacheon-myeon area of South Korea. A landslide inventory map including a total of 762 landslides was compiled based on reports and aerial photograph interpretations. The landslides were randomly separated into two datasets: 70% of landslides were selected for the model establishment and 30% were used for validation purposes. Additionally, 20 landslide condition factors divided into five categories (topographic factors, hydrological factors, soil map, geological map, and forest map) were considered in the landslide susceptibility mapping. The relationships among landslide occurrence and landslide conditioning factors were analyzed and the landslide susceptibility maps were calculated and drawn using the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models. Finally, the maps were validated using the area under the curve (AUC) method. The Multiclass Classifier method had higher prediction accuracy (85.9%) than the Bagging (AUC = 85.4%), LogitBoost (AUC = 84.8%), and AdaBoost (84.0%) methods. View Full-Text
Keywords: landslide susceptibility; Korea; AdaBoost; Bagging; LogitBoost; Multiclass classification landslide susceptibility; Korea; AdaBoost; Bagging; LogitBoost; Multiclass classification
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Kadavi, P.R.; Lee, C.-W.; Lee, S. Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping. Remote Sens. 2018, 10, 1252.

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