• Journal of Korean Society of Coastal and Ocean Engineers
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• v.26 no.6
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• pp.405-412
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• 2014

A number of algal bloom models (red-tide models) have been developed and applied to simulate the redtide growth and decline patterns as the interest on the phytoplankton blooms has been continuously increased. The quantitative error analysis of the model is of great importance because the accurate prediction of the red-tide occurrence and transport pattern can be used to setup the effective mitigations and counter-measures on the coastal ecosystem, aquaculture and fisheries damages. The word "red-tide model" is widely used without any clear definitions and references. It makes the comparative evaluation of the ecological models difficult and confusable. It is highly required to do the performance test of the red-tide models based on the suitable classification and appropriate error analysis because model structures are different even though the same/similar words (e.g., red-tide, algal bloom, phytoplankton growth, ecological or ecosystem models) are used. Thus, the references on the model classification are suggested and the advantage and disadvantage of the models are also suggested. The processes and methods on the performance test (quantitative error analysis) are recommend to the practical use of the red-tide model in the coastal seas. It is suggested in each stage of the modeling procedures, such as verification, calibration, validation, and application steps. These suggested references and methods can be attributed to the effective/efficient marine policy decision and the coastal ecosystem management plan setup considering the red-tide and/or ecological models uncertainty.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.26 no.4
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• pp.217-224
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• 2014

Cochlodinium polykrikoides is a typical harmful algal species which generates the red-tide in the coastal zone, southern Korea. Accurate algal growth model can be established and then the prediction of the red-tide occurrence using this model is possible if the information on the optimal growth model parameters are available because it is directly related between the red-tide occurrence and the rapid algal bloom. However, the limitation factors on the algal growth, such as light intensity, water temperature, salinity, and nutrient concentrations, are so diverse and also the limitation function types are diverse. Thus, the study on the algal growth model development using the available laboratory data set on the growth rate change due to the limitation factors are relatively very poor in the perspective of the model. In this study, the growth model on the C. polykrikoides are developed and suggested as the optimal model which can be used as the element model in the red-tide or ecological models. The optimal parameter estimation and an error analysis are carried out using the available previous research results and data sets. This model can be used for the difference analysis between the lab. condition and in-situ state because it is an optimal model for the lab. condition. The parameter values and ranges also can be used for the model calibration and validation using the in-situ monitoring environmental and algal bloom data sets.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.777-786
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• 2018

This study propose a new method to detect Cochlodinium polykrikoides on satellite images using logistic regression and decision tree. We used spectral profiles(918) extracted from red tide, clear water and turbid water as training data. The 70% of the entire data set was extracted and used for model training, and the classification accuracy of the model was evaluated by using the remaining 30%. As a result of the accuracy evaluation, the logistic regression model showed about 97% classification accuracy, and the decision tree model showed about 86% classification accuracy.

• Journal of Korean Tunnelling and Underground Space Association
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• v.3 no.3
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• pp.3-13
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• 2001

The behavior of the masonry lining is studied to gain basic information on how to reinforce the masonry tunnels. Apart from the previous works on the masonry structures, the multi-course masonry structure, realistic in field condition, is considered and the constitutive relationship of the masonry is, therefore, established. The design charts of the orthotropic material properties are proposed according to the stiffness ratio and the crack initiation and subsequent propagation model is also considered to model the brittle nature of the masonry. A numerical analysis on the masonry panel is investigated to verify the proposed model and future works of the masonry lining are briefly explained.

• Journal of Wetlands Research
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• v.13 no.2
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• pp.363-375
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• 2011

Recently, Water quiality of urban river largely have gotten better by virtue of sewer pipe laying and sewage treatment plants construction. or the various contaminants which is flowed in into river have generated underwater ecosystem disturbance and red tide by lack of sewage and waste water disposal facilities. With tidal river, taehwa river of ulsan metropolitan city has large river width and gradual stream bed gradient at the dry and storage period. Moreover, the flow is paralyzed due to the bridge pier protection work, consist of the mat foundation which is about 1.2km from two bridge and the contaminant is accumulated. it is caused by of the red tide generated from the several years or it activates. In this study, When flow area is largest by changing independent footing of bridge pier of two bridges and using RMA2 model, we hydraulically analyzed a variable breadth of velocity and discharge. Consequently, flow rate increased the maximum 103%, discharge was exposed to increase the maximum 61%. Directly this cannot extinguish the red tide but suppresses the red tide occurrence or can reduce. And it is determined to prevent the depositioning of the contaminant and can control fundamentally the red tide occurrence cause.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1353-1364
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• 2018

This study proposed a method to detect Cochlodinium polykrikoides red tide pixels in satellite images using a logistic regression model of machine learning technique under Imbalanced data. The spectral profiles extracted from red tide, clear water, and turbid water were used as training dataset. 70% of the entire data set was extracted and used for as model training, and the classification accuracy of the model was evaluated using the remaining 30%. At this time, the white noise was added to the spectral profile of the red tide, which has a relatively small number of data compared to the clear water and the turbid water, and over-sampling was performed to solve the unbalanced data problem. As a result of the accuracy evaluation, the proposed algorithm showed about 94% classification accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1161-1170
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• 2019

In this study, we propose a model for predicting Cochlodinium polykrikoides red tide occurrence using deep neural networks. A deep neural network with eight hidden layers was constructed to predict red tide occurrence. The 59 marine and meteorological factors were extracted and used for neural network model training using satellite reanalysis data and meteorological model data. The red tide occurred in the entire dataset is very small compared to the case of no red tide, resulting in an unbalanced data problem. In this study, we applied over sampling with adding noise based data augmentation to solve this problem. As a result of evaluating the accuracy of the model using test data, the accuracy was about 97%.

• Journal of the Earthquake Engineering Society of Korea
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• v.16 no.1
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• pp.47-59
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• 2012

The seismic performance of masonry-infilled frame structures, typical in school buildings, is evaluated through equivalent strut models. A bare frame model, concentric strut models and eccentric strut models with various material characteristics available in the literature are analyzed. Displacements and damage states at the performance points obtained by the capacity spectrum method show great differences among the models. Infill walls act positively in concentric strut models and negatively in eccentric strut models at the performance points for a given seismic demand. In addition, the behavior at the ultimate displacements shows considerably different strengths, inter-story drifts, and numbers and locations of damaged members among various modeling methods and material strengths.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.12 no.1
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• pp.107-118
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• 1994

As satellite altimetry is being progressed to apply with heigher precision to maginal seas, it was necessary to improve correction procedures for tidal signals in altimetry with more accurate tidal model than well-known model of Schwiderski for studying marginal sea dynamics. As a first step, tidal regime of semidiurnal tides$(M_2,\;S_2,\;N_2,\;K_2)$ and diurnal tides$(K_1,\;O_1,\;P_1,\;Q_1)$ were computed with a finer details of formulation of tidal model over the East Asian Marginal Seas covering the Okhotsk Sea and South China Sea and part of Northwest Pacific Ocean with mesh resolutions of 1/6$^{\circ}$. Subsequently the computed sets of harmonic constants from the model were used to remove the tide in selected Sea Surface Heights from Geosat in the modelled region. Preliminary correction procedure suggested in the present study may be extensively used for obtaining Sea Surface Topography over the East Asian Marginal Seas, especially for the region where Schwiderski's harmonic constants are not available.