• Journal of the Korean earth science society
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• v.39 no.5
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• pp.436-457
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• 2018

In this study, we apply a three-dimensional circulation model, MOHID (MOdelo $HIDrodin{\hat{a}}mico$), and reproduce oceanic variation around the East Asian seas including Korea in 2016. Simulation results are verified by using objective analysis fields (EN4, ARMOR3D, AVISO, and SIO products) and in-situ observation data (serial oceanographic and buoy data). Verification results show that general characteristics of the water temperature, sea level anomaly, surface velocity, and mixed layer depths simulated by MOHID are similar with those of the objective analysis fields in the East Asian seas. Especially, when buoy data in the coastal areas are compared, correlation coefficients of sea surface temperature and sea level anomaly are both over 0.8 and normalized standard deviations are between 0.85 and 1.15, respectively. However, it is analyzed that additional improvement would be necessary in the representation of thermocline structure in the East Sea and strong stratification phenomena in the Yellow and South Sea in summer.

• International Journal of Ocean System Engineering
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• v.4 no.1
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• pp.29-42
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• 2014

The objective of this paper is to present the implementation of a joint modeling system able to evaluate the propagation of the polluting agents in the marine environment. The system is composed by circulation model (Mohid) and a spectral wave model (SWAN). The results coming from the circulation model are provided as input to the SWAN simulations. Following this target the Mohid water circulation model was implemented and calibrated in the Black Sea basin. The current simulations were run for one year (2010) with a time step of 24 hours, using wind fields from ECMWF. The results concerning the current fields were introduced into SWAN, and the difference between the results of the SWAN simulations with and without the current input from Mohid was assessed. In this regard, 10 points where the significant wave height difference is higher were considered and analyzed. The conclusion of the work is that such a joint system provides more reliable results concerning the wave and current conditions in the Black Sea as it is very useful in providing the support in the case of the environmental alerts that may occur in marine environments.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.1
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• pp.11-18
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• 2022

In order to improve the accuracy of particle tracking prediction techniques near the Korean Strait, this study compared and analyzed a particle tracking model based on a seawater flow numerical model and a machine learning based on a particle tracking model using field observation data. The data used in the study were the surface drifter buoy movement trajectory data observed in the Korea Strait, prediction data by machine learning (linear regression, decision tree) using the tide and wind data from three observation stations (Gageo Island, Geoje Island, Gyoboncho), and prediciton data by numerical models (ROMS, MOHID). The above three data were compared through three error evaluation methods (Correlation Coefficient (CC), Root Mean Square Errors (RMSE), and Normalized Cumulative Lagrangian Separation (NCLS)). As a final result, the decision tree model had the best prediction accuracy in CC and RMSE, and the MOHID model had the best prediction results in NCLS.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.184-207
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• 2020

This study aimed to determine candidate areas for tidal current-pumped storage hybrid power plants using GIS-based site selection analysis. The study area is the southwestern sea surrounding Jindo Island in South Korea. Factors to be considered for the site selection analysis were derived considering the design and installation characteristics of the hybrid power plant. Numerical simulation to predict tidal speed was performed using the MOHID(Modelo HIDrodin?mico) and the results were converted into spatial data. Subsequently, a GIS-based overlay analysis method proposed in this study was applied to derive the installation candidate area. A total of 10 regions were identified as candidate sites. Among them, it was determined that the power generator could be installed in relatively wide sea areas in Jindo, Seongnamdo, and Hajodo.

• Proceedings of KOSOMES biannual meeting
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• 2017.11a
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• pp.2-2
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• 2017

• Journal of the Korea Convergence Society
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• v.8 no.10
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• pp.45-52
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• 2017

Drifter is an equipment for observing the characteristics of seawater in the ocean, and it can be used to predict effluent oil diffusion and to observe ocean currents. In this paper, we design models or the prediction of drifter trajectory using machine learning. We propose methods for estimating the trajectory of drifter using support vector regression, radial basis function network, Gaussian process, multilayer perceptron, and recurrent neural network. When the propose mothods were compared with the existing MOHID numerical model, performance was improve on three of the four cases. In particular, LSTM, the best performed method, showed the imporvement by 47.59% Future work will improve the accuracy by weighting using bagging and boosting.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.57-67
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• 2018

The ensemble is a unified approach used for getting better performance by using multiple algorithms in machine learning. In this paper, we introduce boosting and bagging, which have been widely used in ensemble techniques, and design a method using support vector regression, radial basis function network, Gaussian process, and multilayer perceptron. In addition, our experiment was performed by adding a recurrent neural network and MOHID numerical model. The drifter data used for our experimental verification consist of 683 observations in seven regions. The performance of our ensemble technique is verified by comparison with four algorithms each. As verification, mean absolute error was adapted. The presented methods are based on ensemble models using bagging, boosting, and machine learning. The error rate was calculated by assigning the equal weight value and different weight value to each unit model in ensemble. The ensemble model using machine learning showed 61.7% improvement compared to the average of four machine learning technique.

• Journal of the Korean earth science society
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• v.44 no.4
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• pp.255-263
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• 2023

Western coastal area of Chungnam, including Cheonsu Bay and Garorim Bay, has suffered from hot and cold extremes. In this study, the extreme sea surface temperature on the western coast of Chungnam was analyzed using the quantile regression method, which extracts the linear regression values in all quantiles. The regional MOHID (MOdelo HIDrodinâmico) model, with a high resolution on a 1/60° grid, was constructed to reproduce the extreme sea surface temperature. For future prediction, the SSP5-8.5 scenario data of the CMIP6 model were used to simulate sea surface temperature variability. Results showed that the extreme sea surface temperature of Cheonsu Bay in August 2017 was successfully simulated, and this extreme sea surface temperature had a significant negative correlation with the Pacific decadal variability index. As a result of future climate prediction, it was found that an average of 2.9℃ increased during the simulation period of 86 years in the Chungnam west coast and there was a seasonal difference (3.2℃ in summer, 2.4℃ in winter). These seasonal differences indicate an increase in the annual temperature range, suggesting that extreme events may occur more frequently in the future.