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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...
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| Publié dans: | Sensors (Basel) |
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| Auteurs principaux: | , , , , , , , , , |
| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
MDPI
2018
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| Sujets: | |
| Accès en ligne: | 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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