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
Main Authors: Bui, D. T., Shahabi, H., Shirzadi, A., Chapi, K., Pradhan, B., Chen, W., Khosravi, K., Panahi, M., Ahmad, B. B., Saro, L.
Format: Article
Language:English
Published: MDPI AG 2018
Subjects:
Online Access:http://eprints.utm.my/id/eprint/79693/1/BaharinAhmad2018_LandSubsidenceSusceptibilityMappinginSouthKorea.pdf
http://eprints.utm.my/id/eprint/79693/
http://dx.doi.org/10.3390/s18082464
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Institution: Universiti Teknologi Malaysia
Language: English