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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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| 出版年: | Sensors (Basel) |
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| 主要な著者: | , , , , , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
MDPI
2018
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| 主題: | |
| オンライン･アクセス: | 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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