• Ocean and Polar Research
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• v.42 no.3
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• pp.195-210
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• 2020

The observed time series from the Korea Ocean Research Stations (KORS) in the Yellow and East China Seas (YECS) have various sources of noise, including bio-fouling on the underwater sensors, intermittent depletion of power, cable leakage, and interference between the sensors' signals. Besides these technical issues, intricate waves associated with background tidal currents tend to result in substantial oscillations in oceanic time series. Such technical and environmental issues require a regionally optimized automatic quality control (QC) procedure. Before the achievement of this ultimate goal, we examined the approach of the Ocean Observatories Initiative (OOI)'s standard QC to investigate whether this procedure is pertinent to the KORS. The OOI QC consists of three categorized tests of global/local range of data, temporal variation including spike and gradient, and sensor-related issues associated with its stuck and drift. These OOI QC algorithms have been applied to the water temperature time series from the Ieodo station, one of the KORS. Obvious outliers are flagged successfully by the global/local range checks and the spike check. Both stuck and drift checks barely detected sensor-related errors, owing to frequent sensor cleaning and maintenance. The gradient check, however, fails to flag the remained outliers that tend to stick together closely, as well as often tend to mark probably good data as wrong data, especially data characterized by considerable fluctuations near the thermocline. These results suggest that the gradient check might not be relevant to observations involving considerable natural fluctuations as well as technical issues. Our study highlights the necessity of a new algorithm such as a standard deviation-based outlier check using multiple moving windows to replace the gradient check and an additional algorithm of an inter-consistency check with a related variable to build a standard QC procedure for the KORS.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.565-575
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• 2022

Recently, in the field of water resource engineering, interest in predicting time series water levels and flow rates using deep learning technology that has rapidly developed along with the Fourth Industrial Revolution is increasing. In addition, although water-level and flow-rate prediction have been performed using the Long Short-Term Memory (LSTM) model and Gated Recurrent Unit (GRU) model that can predict time-series data, the accuracy of flow-rate prediction in rivers with rapid temporal fluctuations was predicted to be very low compared to that of water-level prediction. In this study, the Paldang Bridge Station of the Han River, which has a large flow-rate fluctuation and little influence from tidal waves in the estuary, was selected. In addition, time-series data with large flow fluctuations were selected to collect water-level and flow-rate data for 2 years and 7 months, which are relatively short in data length, to be used as training and prediction data for the LSTM and GRU models. When learning time-series water levels with very high time fluctuation in two models, the predicted water-level results in both models secured appropriate accuracy compared to observation water levels, but when training rapidly temporal fluctuation flow rates directly in two models, the predicted flow rates deteriorated significantly. Therefore, in this study, in order to accurately predict the rapidly changing flow rate, the water-level data predicted by the two models could be used as input data for the rating curve to significantly improve the prediction accuracy of the flow rates. Finally, the results of this study are expected to be sufficiently used as the data of flood warning system in urban rivers where the observation length of hydrological data is not relatively long and the flow-rate changes rapidly.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.47-56
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• 2014

Recently, the velocity of sea-level rising has increased due to the global warming and the natural disasters have been occurred many times. Therefore, there are various demands for the integration of vertical reference datums for the ocean and land areas in order to develop a coastal area and prevent a natural disaster. Currently, the vertical datum for the ocean area refers to Local Mean Sea Level(LMSL) and the vertical datum for the land area is based on Incheon Mean Sea Level(IMSL). This study uses 31 points of Tidal Gauge Bench Mark (TGBM) in order to compares and analyzes the geometric heights referring LMSL, IMSL, and the nationally determined geoid surface. 11 points of comparable data are biased more than 10 cm when the geometric heights are compared. It seems to be caused by the inflow of river, the relocation of Tidal Gauge Station, and the topographic change by harbor construction. Also, this study analyze the inclination of sea surface which is the difference between IMSL and LMSL, and it shows the inclination of sea surface increases from the western to southern, and eastern seas. In this study, it is shown that TGBM can be used to integrate vertical datums for the ocean and land areas. In order to integrate the vertical datums, there need more surveying data connecting the ocean to the land area, also cooperation between Korea Hydrographic and Oceanographic Administration and National Geographic Information Institute. It is expected that the integrated vertical datum can be applied to the development of coastal area and the preventative of natural disaster.