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Article

Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

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Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
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Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
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Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia
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Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
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College of Geology & Environment, Xi’an University of Science and Technology, Xi’an 710054, China
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Department of Watershed Sciences Engineering, Faculty of Natural Resources, Sari Agricultural and Natural Resources University (SANRU), Sari, Mazandaran P.O.BOX 48181-68984, Iran
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Department of Geophysics, Young Researchers and Elites Club, North Tehran Branch, Islamic Azad University, Tehran P.O. Box 19585/466, Iran
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10 Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia
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Geological Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Korea
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Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Korea
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Authors to whom correspondence should be addressed.
Sensors 2018, 18(8), 2464; https://doi.org/10.3390/s18082464
Received: 29 June 2018 / Revised: 24 July 2018 / Accepted: 27 July 2018 / Published: 31 July 2018
In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results. View Full-Text
Keywords: land subsidence; machine learning algorithms; GIS; South Korea land subsidence; machine learning algorithms; GIS; South Korea
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MDPI and ACS Style

Tien Bui, D.; Shahabi, H.; Shirzadi, A.; Chapi, K.; Pradhan, B.; Chen, W.; Khosravi, K.; Panahi, M.; Bin Ahmad, B.; Saro, L. Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms. Sensors 2018, 18, 2464. https://doi.org/10.3390/s18082464

AMA Style

Tien Bui D, Shahabi H, Shirzadi A, Chapi K, Pradhan B, Chen W, Khosravi K, Panahi M, Bin Ahmad B, Saro L. Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms. Sensors. 2018; 18(8):2464. https://doi.org/10.3390/s18082464

Chicago/Turabian Style

Tien Bui, Dieu; Shahabi, Himan; Shirzadi, Ataollah; Chapi, Kamran; Pradhan, Biswajeet; Chen, Wei; Khosravi, Khabat; Panahi, Mahdi; Bin Ahmad, Baharin; Saro, Lee. 2018. "Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms" Sensors 18, no. 8: 2464. https://doi.org/10.3390/s18082464

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