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Land subsidence susceptibility mapping in South Korea using machine learning algorithms

Bui, D. T. and Shahabi, H. and Shirzadi, A. and Chapi, K. and Pradhan, B. and Chen, W. and Khosravi, K. and Panahi, M. and Ahmad, B. B. and Saro, L. (2018) Land subsidence susceptibility mapping in South Korea using machine learning algorithms. Sensors (Switzerland), 18 (8). ISSN 1424-8220

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Official URL: http://dx.doi.org/10.3390/s18082464

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.

Item Type:Article
Uncontrolled Keywords:GIS, Land subsidence, Machine learning algorithms, South korea
Subjects:G Geography. Anthropology. Recreation > G Geography (General) > G70.212-70.215 Geographic information system
Divisions:Geoinformation and Real Estate
ID Code:79693
Deposited By: Fazli Masari
Deposited On:28 Jan 2019 14:38
Last Modified:28 Jan 2019 14:38

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