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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Detalles Bibliográficos
Autores principales: Tien Bui, Dieu, Shahabi, Himan, Shirzadi, Ataollah, Chapi, Kamran, Pradhan, Biswajeet, Chen, Wei, Khosravi, Khabat, Panahi, Mahdi, Bin Ahmad, Baharin, Saro, Lee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111310/
https://www.ncbi.nlm.nih.gov/pubmed/30065216
http://dx.doi.org/10.3390/s18082464

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111310/
https://www.ncbi.nlm.nih.gov/pubmed/30065216
http://dx.doi.org/10.3390/s18082464

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