Land subsidence susceptibility mapping in South Korea using machine learning algorithms

• 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: G70.212-70.215 Geographic information system
• 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
• Institution: Universiti Teknologi Malaysia

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...