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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Bibliografiset tiedot
Julkaisussa:Sensors (Basel)
Päätekijät: Tien Bui, Dieu, Shahabi, Himan, Shirzadi, Ataollah, Chapi, Kamran, Pradhan, Biswajeet, Chen, Wei, Khosravi, Khabat, Panahi, Mahdi, Bin Ahmad, Baharin, Saro, Lee
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: MDPI 2018
Aiheet:
Article
Linkit: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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