• Proceedings of the Korean Society of Fisheries Technology Conference
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• 2001.10a
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• pp.204-205
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• 2001

Manful algae, Cochlodinium polykrikoides has damaged to fisheries organisms by making massive blooms mainly in the South Sea during the higher water temperature season since 1995 in Korea. Ecological and hydrodynamic studies of the species offer useful information in understanding its bloom mechanism giving promising data for the modeling and prediction of the blooms. (omitted)

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1167-1181
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• 2021

In relation to the algae bloom, four types of blue-green algae that emit toxic substances are designated and managed as harmful Cyanobacteria, and prediction information using a physical model is being also published. However, as algae are living organisms, it is difficult to predict according to physical dynamics, and not easy to consider the effects of numerous factors such as weather, hydraulic, hydrology, and water quality. Therefore, a lot of researches on algal bloom prediction using machine learning have been recently conducted. In this study, the characteristic importance of water quality factors affecting the occurrence of Cyanobacteria harmful algal blooms (CyanoHABs) were analyzed using the random forest (RF) model for Bohyeonsan Dam and Yeongcheon Dam located in Yeongcheon-si, Gyeongsangbuk-do and also predicted the occurrence of harmful blue-green algae using the machine learning and deep learning models and evaluated their accuracy. The water temperature and total nitrogen (T-N) were found to be high in common, and the occurrence prediction of CyanoHABs using artificial neural network (ANN) also predicted the actual values closely, confirming that it can be used for the reservoirs that require the prediction of harmful cyanobacteria for algal management in the future.

• Korean Journal of Environmental Biology
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• v.37 no.3
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• pp.433-445
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• 2019

This study was designed to develop simple tools to easily and efficiently predict the occurrence of algal bloom in agricultural lakes. Physicochemical water quality parameters were examined to reflect the phytoplankton productivity in 182 samples collected from 15 agricultural lakes from April to October 2018. Total phytoplankton abundance was significantly correlated with chlorophyll-a (Chl-a) (r=0.666) and Secchi depth (SD) (r= -0.351). The abundances of cyanobacteria and harmful cyanobacteria were also correlated with Chl-a (r=0.664, r=0.353) and SD (r= -0.340, r= -0.338), respectively, but not with total nitrogen (TN) and total phosphorus (TP). The Chl-a concentration was correlated with SD (r= -0.434), showing a higher similarity than phytoplankton abundance. Therefore, Chl-a and SD were selected as diagnostic factors for algal bloom prediction, instead of analyzing the standing crop of harmful cyanobacteria used in algae alarm systems. Specifically, accurate diagnoses were made using realtime SD measurements. The algal bloom diagnostic tool is an inverse cone-shaped container with an algal bloom diagnosis chart that modified SD and turbidity measurement methods. Lake water was collected to observe the number of rings visible in the container or the number indicated in each ring, depending on the degree of algal bloom,and to determine the final stage of algal blooming by comparison to the colorimetric level on the diagnosis chart. For an accurate diagnosis, we presented 4-step diagnostic criteria based on the concentration of Chl-a and the number of rings and a fan-shaped algal bloom diagnosis chart with Hexa code names. This tool eliminated the variables and errors of previous methods and the results were easily interpreted. This study is expected to facilitate the diagnosis of algal bloom in agricultural lakes and the establishment of an efficient algal bloom management plan.

• Journal of Korean Society on Water Environment
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• v.29 no.1
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• pp.19-28
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• 2013

Artificial neural networks were used for time series modelling of algal dynamics of whole year and by season at the Paldang dam station (confluence area). The modelling was based on comprehensive weekly water quality data from 1997 to 2004 at the Paldang dam station. The results of validation of seasonal models showed that the timing and magnitude of the observed chlorophyll a concentration was predicted better, compared with the ANN model for whole year. Internal weightings of the inputs in trained neural networks were obtained by sensitivity analysis for identification of the primary driving mechanisms in the system dynamics. pH, COD, TP determined most the dynamics of chlorophyll a, although these inputs were not the real driving variable for algal growth. Short-term prediction models that perform one or two weeks ahead predictions of chlorophyll a concentration were designed for the application of Harmful Algal Alert System in Lake Paldang. Short-term-ahead ANN models showed the possibilities of application of Harmful Algal Alert System after increasing ANN model's performance.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.1881-1888
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• 2011

Red tide is a natural phenomenon to bloom harmful algal, which fish and shellfish die en masse. Red tide damage with respect to sea farming has been occurred each year. Red tide damage can be minimized by means of prediction of red tide blooms. Red tide prediction using naive bayes classifier can be achieve good prediction results. The result of naive bayes method only determine red tide blooms, whereas the method can not know how increasing of red tide algae density. In this paper, we proposed the red tide blooms prediction method using fuzzy reasoning and naive bayes classifier. The proposed method can enhance the precision of red tide prediction and forecast the increasing density of red tide algae.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.34 no.5
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• pp.536-544
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• 2001

Three harmful algal bloom species with similar morphology, Cochlodinium polykrikoides, Gyodinium impudicum and Gymodinium catenatum have damaged to aquatic animals or human health by either making massive blooms or intoxication of shellfishes in a food chain. Eco-physiological and hydrodynamic studies on the harmful algae offer useful informations in the understanding their bloom mechanism by giving promising data for the prediction and modelling of harmful algal blooms event. Thus, we studied the abundance of these species in the coastal area of South Sea of Korea and their effects of temperature, salinity, irradiance and nutrient on the growth for the isolates. The timing for initial appearance of the three species around the coastal area of Namhaedo, Narodo and Wando was between Bate July and late August in 1999 when water temperature ranged from $22.8^{\circ}C\;to\;26.5^{\circ}C$ Vegetative cells of C. polykrikoides and G. impudicum were abundant until late September when water temperature had been dropped to less than $23^{\circ}C$. By contrast, vegetative cell of G. catenatum disappeared before early September, showing shorter period of abundance than the other two species in the South Sea. Both G. impudicum and G. catenatum revealed comparatively low density with a maximal cell density of 3,460 cells/L and 440 cells/L, respectively without making any bloom, while C. polykrikoides made massive blooms with a maximal cell density more than $40\times10^6$cells/L, The three species showed a better growth at the relatively higher water temperature ranging from 22 to $28^{\circ}C$ with their maximal growth rate at $25^{\circ}C$ in culture, which almost corresponded with the water temperature during the outbreak of C. polykrikoides in the coastal area of South Sea. Also, they all showed a relatively higher growth at the salinity from 30 to $35\%$. Specially, G. impudicum showed the euryhalic characteristics among the species, On the other hand, growth rate of G. catenatum decreased sharply with the increase of water temperature at the experimental ranges more than $35\%$. The higher of light intensities showed the better growth rates for the three species, Moreover, C. polykrikoides and G. impudirum continued their exponential growth even at 7,500 lux, the highest level of light intensity in the experiment, Therefore, It is assumed that C. polykrikoides has a physiological capability to adapt and utilize higher irradiance resulting in the higher growth rate without any photo inhibition response at the sea surface where there is usually strong irradiance during its blooming season. Although C. poiykikoides and G. impudicum continued their linear growth with the increase of nitrate ($NO_3^-$) and ammonium ($NH_4^-$) concentrations at less than the $40{\mu}M$, they didn't show any significant differences in growth rates with the increase of nitrate and ammonium concentrations at more than $40{\mu}M$, signifying that the nitrogen critical point for the growth of the two species stands between 13.5 and $40{\mu}M$. Also, even though both of the two species continued their linear growth with the increase of phosphate ($PO_4^{2-}$) concentrations at less than the $4.05{\mu}M$, there were no any significant differences in growth rates with the increase of phosphate concentrations at more than $4.05{\mu}M$, signifying that the phosphate critical point for the growth of the two species stands between 1.35 and $4.05{\mu}M$. On the other hand, C. polykrikoides has made blooms at the oligotrophic environment near Narodo and Namhaedo where the concentration of DIN and DIP are less than 1.2 and $0.3{\mu}M$, respectively. We attributed this phenomenon to its own ecological characteristics of diel vertical migration through which C. polykrikoides could uptake enough nutrients from the deep sea water near bottom during the night time irrespective of the lower nutrient pools in the surface water.

• Journal of Korean Society on Water Environment
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• v.40 no.3
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• pp.121-129
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• 2024

Harmful cyanobacterial blooms (HCBs) are caused by the rapid proliferation of cyanobacteria and are believed to be exacerbated by climate change. However, the extent to which HCBs will be stimulated in the future due to increased temperature remains uncertain. This study aims to predict the future occurrence of cyanobacteria in the Nakdong River, which has the highest incidence of HCBs in South Korea, based on temperature rise scenarios. Representative Concentration Pathways (RCPs) were used as the basis for these scenarios. Data-driven model simulations were conducted, and out of the four machine learning techniques tested (multiple linear regression, support vector regressor, decision tree, and random forest), the random forest model was selected for its relatively high prediction accuracy. The random forest model was used to predict the occurrence of cyanobacteria. The results of boxplot and time-series analyses showed that under the worst-case scenario (RCP8.5 (2100)), where temperature increases significantly, cyanobacterial abundance across all study areas was greatly stimulated. The study also found that the frequencies of HCB occurrences exceeding certain thresholds (100,000 and 1,000,000 cells/mL) increased under both the best-case scenario (RCP2.6 (2050)) and worst-case scenario (RCP8.5 (2100)). These findings suggest that the frequency of HCB occurrences surpassing a certain threshold level can serve as a useful diagnostic indicator of vulnerability to temperature increases caused by climate change. Additionally, this study highlights that water bodies currently susceptible to HCBs are likely to become even more vulnerable with climate change compared to those that are currently less susceptible.

• ALGAE
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• v.34 no.3
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• pp.237-251
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• 2019

The dinoflagellate genus Alexandrium is known to often form harmful algal blooms causing human illness and large-scale mortality of marine organisms. Therefore, the population dynamics of Alexandrium species are of primary concern to scientists and aquaculture farmers. The growth rate of the Alexandrium species is the most important parameter in prediction models and nutrient conditions are critical parameters affecting the growth of phototrophic species. In Korean coastal waters, Alexandrium affine and Alexandrium fraterculus, of similar sizes, often form red-tide patches together. Thus, to understand bloom dynamics of A. affine and A. fraterculus, growth rates and nitrate uptake of each species as a function of nitrate ($NO_3$) concentration at $100{\mu}mol\;photons\;m^{-2}s^{-1}$ under 14-h light : 10-h dark and continuous light conditions were determined using a nutrient repletion method. With increasing $NO_3$ concentration, growth rates and $NO_3$ uptake of A. affine or A. fraterculus increased, but became saturated. Under light : dark conditions, the maximum growth rates of A. affine and A. fraterculus were 0.45 and $0.42d^{-1}$, respectively. However, under continuous light conditions, the maximum growth rate of A. affine slightly increased to $0.46d^{-1}$, but that of A. fraterculus largely decreased. Furthermore, the maximum nitrate uptake of A. affine and A. fraterculus under light : dark conditions were 12.9 and $30.1pM\;cell^{-1}d^{-1}$, respectively. The maximum nitrate uptake of A. affine under continuous light conditions was $16.4pM\;cell^{-1}d^{-1}$. Thus, A. affine and A. fraterculus have similar maximum growth rates at the given $NO_3$ concentration ranges, but they have different maximum nitrate uptake rates. A. affine may have a higher conversion rate of $NO_3$ to body nitrogen than A. fraterculus. Moreover, a longer exposure time to the light may confer an advantage to A. affine over A. fraterculus.

• 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%.