Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

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ID: 261241
2018
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Abstract
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.
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bui2018sensorsland Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Dieu Tien Bui;Himan Shahabi;Ataollah Shirzadi;Kamran Chapi;Biswajeet Pradhan;Wei Chen;Khabat Khosravi;Mahdi Panahi;Baharin Bin Ahmad;Lee Saro;Tien Bui, Dieu;Shahabi, Himan;Shirzadi, Ataollah;Chapi, Kamran;Pradhan, Biswajeet;Chen, Wei;Khosravi, Khabat;Panahi, Mahdi;Bin Ahmad, Baharin;Saro, Lee;
Journal sensors
Year 2018
DOI
10.3390/s18082464
URL
https://dx.doi.org/10.3390/s18082464
Keywords
gis machine learning algorithms south korea land subsidence

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