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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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書誌詳細
主要な著者:	Al-Rawashdeh, Mohammad, Al Nawaiseh, Moh’d, Yousef, Isam, Bisharah, Majdi, Alkhadrawi, Sajeda, Al-Bdour, Hamza
フォーマット:	論文
出版事項:	Springer Cham 2024
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
タグ:	タグ追加タグなし, このレコードへの初めてのタグを付けませんか!

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