• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.47-55
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• 2022

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.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.621-629
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• 2019

A logical measure is suggested to estimate an accurate Sound Speed Profile (SSP) for the unusual variation of salinity in the Yellow Sea. Based on National Aeronautics and Space Administration (NASA)'s Aqua and Soil Moisture Active Passive (SMAP) satellite data, this measure identifies the area of temperature inversion effect and expansion of low salinity (<30.5 psu) water. Subsequently, on the area, the Conductivity, Temperature, and Depth (CTD) mounted unmanned maritime system estimates accurate SSP. In order to carry out this measure conveniently, a flow chart is demonstrated in this research. By using this measure which finds the high variational salinity area, the inaccuracy issue for calculating SSP from Expandable Bathy Thermograph (XBT) is expected to be solved.