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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...

תיאור מלא

שמור ב:
מידע ביבליוגרפי
הוצא לאור ב:Sensors (Basel)
Main Authors: Tien Bui, Dieu, Shahabi, Himan, Shirzadi, Ataollah, Chapi, Kamran, Pradhan, Biswajeet, Chen, Wei, Khosravi, Khabat, Panahi, Mahdi, Bin Ahmad, Baharin, Saro, Lee
פורמט: Artigo
שפה:Inglês
יצא לאור: MDPI 2018
נושאים:
גישה מקוונת: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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