• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.

• Journal of the Korean earth science society
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• v.43 no.5
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• pp.604-617
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• 2022

Sea surface temperature (SST) is a key variable that can be used to understand ocean-atmosphere phenomena and predict climate change. Satellite microwave remote sensing enables the measurement of SST despite the presence of clouds and precipitation in the sensor path. Therefore, considering the high utilization of microwave SST, it is necessary to continuously verify its accuracy and analyze its error characteristics. In this study, the validation of the microwave global precision measurement (GPM)/GPM microwave imager (GMI) SST around the Northwest Pacific and Korean Peninsula was conducted using surface drifter temperature data for approximately eight years from March 2014 to December 2021. The GMI SST showed a bias of 0.09K and an average root mean square error of 0.97K compared to the actual SST, which was slightly higher than that observed in previous studies. In addition, the error characteristics of the GMI SST were related to environmental factors, such as latitude, distance from the coast, sea wind, and water vapor volume. Errors tended to increase in areas close to coastal areas within 300 km of land and in high-latitude areas. In addition, relatively high errors were found in the range of weak wind speeds (<6 m s^-1) during the day and strong wind speeds (>10 m s^-1) at night. Atmospheric water vapor contributed to high SST differences in very low ranges of <30 mm and in very high ranges of >60 mm. These errors are consistent with those observed in previous studies, in which GMI data were less accurate at low SST and were estimated to be due to differences in land and ocean radiation, wind-induced changes in sea surface roughness, and absorption of water vapor into the microwave atmosphere. These results suggest that the characteristics of the GMI SST differences should be clarified for more extensive use of microwave satellite SST calculations in the seas around the Korean Peninsula, including a part of the Northwest Pacific.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.13 no.1
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• pp.1-14
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• 2008

Eight comprehensive oceanographic cruises on a squared $30{\times}30\;km$ area have been made to investigate the short and long-term impacts on the water qualities due to the sand mining operations at Exclusive Economic Zone (EEZ) in the central Yellow Sea from 2004 to 2007. The area was categorized to 'Sand Mining Zone', 'Potentially Affected Zone', and 'Reference Zone'. The investigation covered suspended solids, nutrients (nitrate, nitrite, ammonium, phosphate), and chlorophyll-a in seawater and several parameters such as water temperature, salinity, pH, and ORP. Additionally, several intensive water collections were made to trace the suspended solids and other parameters along the turbid water by sand mining activities. The comprehensive investigation showed that suspended solids, nitrate, chlorophyll-a and ORP be sensitively responding parameters of seawater by sand mining operations. The intensive collection of seawater near the sand mining operation revealed that each parameter show different distribution pattern: suspended solids showed an oval-shaped distribution of the north-south direction of 8 km wide and the east-west direction of 5 km wide at the surface and bottom layers. On the other hand, phosphate showed so narrow distribution not to traceable. Also ammonium showed a limited distribution, but its boundary was connected to the high nitrate and chlorophyll-a concentrations with high N/P ratios. From the last 4 years of the comprehensive and intensive investigations, we found that suspended solids, ammonium, nitrate, chlorophyll-a, and ORP revealed the sensitive parameters of water quality for tracing the sand mining operations in seawater. Especially suspended solids and ORP would be useful tracers for monitoring the water qualities of remote area like EEZ in Yellow Sea.