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http://dx.doi.org/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  

Kim, Young-Joo (Dept. of Defense Science, Korea National Defense University)
Lee, Soo-Jin (Dept. of Defense Science, Korea National Defense University)
Kim, Young-Won (Dept. of Defense Science, Korea National Defense University)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.11, 2022 , pp. 47-55 More about this Journal
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;
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Times Cited By KSCI : 3  (Citation Analysis)
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