Prediction of Vertical Sea Water Temperature Profile in the East Sea Based on Machine Learning and XBT Data (XBT 데이터를 이용한 기계학습 기반의 동해 수직 수온분포 예측)

Young-Joo Kim (김영주); Soojin Lee (이수진); Young-won Kim (김영원)

doi:10.9708/jksci.2022.27.11.047

Prediction of Vertical Sea Water Temperature Profile in the East Sea Based on Machine Learning and XBT Data

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2022, 27(11), pp.47-55
DOI : 10.9708/jksci.2022.27.11.047
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : October 11, 2022
Accepted : November 3, 2022
Published : November 30, 2022

Young-Joo Kim ¹, Soojin Lee ¹, Young-won Kim ¹

¹국방대학교

Accredited

ABSTRACT

Recently, researches on the prediction of sea water temperature using artificial intelligence models has been actively conducted in Korea. However, most researches in the sea around the Korean peninsula mainly focus on predicting sea surface temperatures. Unlike previous researches, this research predicted the vertical sea water temperature profile of the East Sea, which is very important in submarine operations and anti-submarine warfare, using XBT(eXpendable Bathythermograph) data and machine learning models(RandomForest, XGBoost, LightGBM). The model was trained using XBT data measured from sea surface to depth of 200m in a specific area of the East Sea, and the prediction accuracy was evaluated through MAE(Mean Absolute Error) and vertical sea water temperature profile graphs.

KEYWORDS

Machine Learning, RandomForest, XGBoost, LightGBM, XBT, Vertical Sea Water Temperature Profile Prediction

Citation status

* References for papers published after 2023 are currently being built.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Prediction of Vertical Sea Water Temperature Profile in the East Sea Based on Machine Learning and XBT Data

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Prediction of Vertical Sea Water Temperature Profile in the East Sea Based on Machine Learning and XBT Data

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KEYWORDS

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* References for papers published after 2023 are currently being built.