Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms
This study compares Bayesian Optimization-based machine learning systems that anticipate earthquake-damaged buildings and to evaluates building damage classification models. Using metrics, this study evaluates Random Forest, ElasticNet, and Decision Tree algorithms. This study showed damage level as...
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| 主要な著者: | , , , , , |
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| フォーマット: | 論文 |
| 出版事項: |
Springer Cham
2024
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| オンライン･アクセス: | http://psasir.upm.edu.my/id/eprint/105841/ https://link.springer.com/article/10.1007/s42107-023-00771-6 |
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