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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Gepubliceerd in:Sensors (Basel)
Hoofdauteurs: Tien Bui, Dieu, Shahabi, Himan, Shirzadi, Ataollah, Chapi, Kamran, Pradhan, Biswajeet, Chen, Wei, Khosravi, Khabat, Panahi, Mahdi, Bin Ahmad, Baharin, Saro, Lee
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: MDPI 2018
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Article
Online toegang:https://ncbi.nlm.nih.gov/pmc/articles/PMC6111310/
https://ncbi.nlm.nih.gov/pubmed/30065216
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s18082464
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