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

• Communications for Statistical Applications and Methods
• /
• v.31 no.3
• /
• pp.309-322
• /
• 2024

Identifying algae in water is important for managing algal blooms which have great impact on drinking water supply systems. There have been various microscopic approaches developed for algae classification. Many of them are based on the morphological features of algae. However, there have seldom been mathematical frameworks for comparing the shape of algae, represented as a planar continuous curve obtained from an image. In this work, we describe a recent framework for computing shape distance between two different algae based on the elastic metric and a novel functional representation called the square root velocity function (SRVF). We further introduce statistical procedures for multiple shapes of algae including computing the sample mean, the sample covariance, and performing the principal component analysis (PCA). Based on the shape distance, we classify six algal species in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis, Oscillatoria, and Anabaena), two diatoms (Fragilaria and Synedra), and one green algae (Pediastrum). We provide and compare the classification performance of various distance-based and model-based methods. We additionally compare elastic shape distance to non-elastic distance using the nearest neighbor classifiers.

• International Journal of Advanced Culture Technology
• /
• v.5 no.1
• /
• pp.64-69
• /
• 2017

The rapid growth of algae occurs can induce the algae bloom when nutrients are supplied from anthropogenic sources such as fertilizer, animal waste or sewage in runoff the water currents or upwelling naturally. The algae blooms creates the human health problem in the environment as well as in the water resource managers including hypoxic dead zones and harmful toxins and pose challenges to water treatment systems. The algal blooms in the source water in water treatment systems affects the drinking water taste & odor while clogging or damaging filtration systems and putting a strain on the systems designed to remove algal toxins from the source water. This paper propose the emerging In-Situ self-diagnosable smart algae sensing device with wireless connectivity for smart remote monitoring and control. In this research, we developed the In-Site Algae diagnosable sensing device with wireless sensor network (WSN) connectivity with Optical Biological Sensor and environmental sensor to monitor the water treatment systems. The proposed system emulated in real-time on the water treatment plant and functional evaluation parameters are presented as part of the conceptual proof to the proposed research.

• ALGAE
• /
• v.19 no.4
• /
• pp.321-327
• /
• 2004

The literature review on the dynamics of the phytoplankton communities in terms of species composition, standing crops, abundant species and dominant species in the lower part of the Han River from 1940s to 2000s was conducted for the prospective prediction of their succession patterns. Total of 326 taxa were identified and they belonged to 47 blue-green algae, 139 green algae, 12 euglenoids, 126 diatoms, 6 din flagellates and 2 silicoflagellates. Composition of phytoplankton communities were 83.6% diatoms, 10.5% blue-green algae and 5.3% green algae in the middle of 1960s, whereas those were 43.2% diatoms, 40.7% green algae and 13.6% blue-green algae in the 1990s. Before 1990s, Synedra ulna, Melosira varians, Cymbella tumida, Synedra acus, Cymbella ventricosa, Navicula cryptocephala, Nitzschia palea, Aulacoseira granulata, Gomphonema parvulum and Cymbella affinis were most frequent, while those after 1990 were Asterionella formosa, Asterionella gracillima, Aulacoseira granulate, Aulacoseira granulata var. angustissima, Chlorella vulgare, Fragilaria crotonensis and Synedra ulna. Phytoplankton blooms were frequent from winter to the late spring and rare in summer due to heavy rain and discharge. Seasonal variations of the dominant species were fairly obvious; Asterionella gracillima and Aulacoseira granulata in spring, Aulacoseira granulate and Aulacoseira granulate var. angustissima in summer and autumn, Asterionella gracillima and Stephan discus hantzschii in winter. Recently blue-green algae, Microcystis, Aphanocapsa, Dactylococcopsis have been more abundant than those of the previous reports. Based on the current situations, Stephan discus hantzschii f. tennis, Asterionella gracillima, Aulacoseira granulate and blue-green algae will be more abundant and blooms of those species will be more frequent.

• ALGAE
• /
• v.34 no.4
• /
• pp.253-266
• /
• 2019

Several species classified to the genus Ulva are primarily responsible for causing green tides all over the world. For almost two decades, green tides have been resulted in numerous ecological problems along the eastern coast of Jeju Island, Korea. In order to characterize the species of Ulva responsible for causing the massive blooms on Jeju Island, we conducted DNA barcoding of tufA and rbcL sequences on 183 specimens of Ulva from eight sites on Jeju Island. The concatenated analysis identified five bloom-forming species: U. australis, U. lactuca, U. laetevirens, U. ohnoi and a novel species, U. pseudo-ohnoi sp. nov. Among them, U. australis, U. lactuca, and U. laetevirens caused to the blooms coming mainly from the substratum. U. ohnoi and U. pseudo-ohnoi sp. nov. were causative the free-floating blooms. Four species, except U. australis, are characterized by marginal teeth. A novel species, U. pseudo-ohnoi sp. nov., is clearly diverged from the U. lactuca, U. laetevirens, and U. ohnoi clade in the concatenated maximum likelihood analysis. Accurate species delimitation will contribute to a management of massive Ulva blooms based on this more comprehensive knowledge.

• ALGAE
• /
• v.17 no.4
• /
• pp.283-292
• /
• 2002

• ALGAE
• /
• v.18 no.4
• /
• pp.361-363
• /
• 2003

This study was conducted at Seomjin River estuary to identify the causative species of algal bloom and their blooming cycles. Field surveys were conducted at 4 stations in every week from April to December of 1999. Thirty species were observed as the causative species of alga bloom. Skeletonema costatum, Thalassiosira sp., and microflagellate spp. (mixed red tide: Chroomonas sp. and two species of Prasinophycea) made algal blooms during the present study period. In addition, toxic algal species of diatom Pseudo-nitzschia multiseries and dinoflagellate Dinophysis acuminata were observed. The algal blooms were caused by microflagellate spp. in June, Thalassiosira sp. in July and Skeletonema costatum in August. Generally, the algal blooms persisted for about 5 days in this area.

• The KIPS Transactions:PartB
• /
• v.18B no.5
• /
• pp.291-294
• /
• 2011

Red tide is a temporary natural phenomenon to change sea color by harmful algal blooms, which finfish and shellfish die en masse. There have been many studies on red tide due to increasing of harmful algae damage of fisheries in Korea. Particularly, red tide damage can be minimized by means of prediction of red tide blooms. However, the most of red tide research in Korea has been focused only classification of red tide which it is not enough for predicting red tide blooms. In this paper, we proposed the red tide blooms prediction method using fuzzy reasoning.

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

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.29 no.5
• /
• pp.435-444
• /
• 2023

Suitable environmental conditions in Saemangeum frequently favor phytoplankton growth. There have been occurrences of sudden phytoplankton blooms, surpassing the algae management standards. A model was designed to prevent such blooms using scientific predictive techniques to forecast and regulate the possibility of phytoplankton blooms. We propose effective and efficient algae control measures concerning every phytoplankton species optimized through the policy control of nutrients (DIN, PO4-P) from rivers and controlling lake salinity using gate operations. The probability of phytoplankton blooms was initially forecast using an artificial neural network algorithm based on observations. The model's Kappa number fluctuated from 0.7889 to 1.0000, indicating good to excellent predictive power. The Garson algorithm was then utilized to assess the significance of explanatory variables for every species. Meanwhile, the probability of phytoplankton blooms was anticipated depending on the DIN and salinity value changes. Therefore, the model predicted the precise DIN and salinity concentrations to inhibit phytoplankton blooms for each species. Hence, the green algae model can create effective proactive measures to avoid future phytoplankton blooms in enormous artificial lakes.