Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2018-07, Vol.18 (8), p.2464
Main Authors: Tien Bui, Dieu, Shahabi, Himan, Shirzadi, Ataollah, Chapi, Kamran, Pradhan, Biswajeet, Chen, Wei, Khosravi, Khabat, Panahi, Mahdi, Bin Ahmad, Baharin, Saro, Lee
Format: Article
Language:English
Subjects:
Artificial intelligence
Bayesian analysis
GIS
land subsidence
Land use
machine learning algorithms
Model accuracy
Modelling
Rock mass rating
South Korea
Statistical analysis
Subsidence
Support vector machines
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s18082464