Machine Learning-Based Anomaly Detection on Seawater Temperature Data with Oversampling

This study deals with a method for anomaly detection in seawater temperature data using machine learning methods with oversampling techniques. Data were acquired from 2017 to 2023 using a Conductivity–Temperature–Depth (CTD) system in the Pacific Ocean, Indian Ocean, and Sea of Korea. The seawater t...

詳細記述

書誌詳細

出版年:	Journal of Marine Science and Engineering
主要な著者:	Hangoo Kang, Dongil Kim, Sungsu Lim
フォーマット:	論文
言語:	英語
出版事項:	MDPI AG 2024-05-01
主題:	machine learning anomaly detection class imbalance problem oversampling data augmentation ocean observation
オンライン･アクセス:	https://www.mdpi.com/2077-1312/12/5/807