Modelling the growth and movement of cyanobacteria in river systems

Güven, Başak; Howard, A. D.

doi:10.1016/j.scitotenv.2006.03.035

Cited by 29 publications

(10 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The findings of this study also corresponded well with the alert system for prediction of cyanobacterial bloom in South Korea [27]. Although it is difficult to decide on the best training algorithm and the number of hidden layer nodes, its advantages lie in that it requires no information on the model structure and developed models were more flexible and adaptable to alternative scenarios [48]. …”

Section: Models For Prediction Of Cyanobacterial Bloomssupporting

confidence: 79%

“…The weights of an artificial neuron can be adjusted using algorithms in order to obtain the desired output from the network. This process of adjusting the weights is called learning or training [48]. Backpropagation is the most common algorithm in which the signals are sent forward and the errors are propagated backwards.…”

Section: Models For Prediction Of Cyanobacterial Bloomsmentioning

confidence: 99%

See 1 more Smart Citation

Status, Alert System, and Prediction of Cyanobacterial Bloom in South Korea

Srivastava

Ahn

Asthana

et al. 2015

BioMed Research International

View full text Add to dashboard Cite

Bloom-forming freshwater cyanobacterial genera pose a major ecological problem due to their ability to produce toxins and other bioactive compounds, which can have important implications in illnesses of humans and livestock. Cyanobacteria such as Microcystis, Anabaena, Oscillatoria, Phormidium, and Aphanizomenon species producing microcystins and anatoxin-a have been predominantly documented from most South Korean lakes and reservoirs. With the increase in frequency of such blooms, various monitoring approaches, treatment processes, and prediction models have been developed in due course. In this paper we review the field studies and current knowledge on toxin producing cyanobacterial species and ecological variables that regulate toxin production and bloom formation in major rivers (Han, Geum, Nakdong, and Yeongsan) and reservoirs in South Korea. In addition, development of new, fast, and high-throughput techniques for effective monitoring is also discussed with cyanobacterial bloom advisory practices, current management strategies, and their implications in South Korean freshwater bodies.

show abstract

Section: Models For Prediction Of Cyanobacterial Bloomssupporting

confidence: 79%

Section: Models For Prediction Of Cyanobacterial Bloomsmentioning

confidence: 99%

Status, Alert System, and Prediction of Cyanobacterial Bloom in South Korea

Srivastava

Ahn

Asthana

et al. 2015

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…Several authors have modeled the growth and movement of cyanobacteria in river systems (Lung & PearL 1988;Maier & DanDy 1997;guven & hoWarD 2006), establishing that the varying flow conditions play a major role in initiating blooms, with the strong vertical mixing generated by high river flows eliminating conditions favorable for cyanobacteria. Mitrovic et al (2003) investigated the critical flow velocity in the Darling River (Australia) for an Anabaena circinalis bloom.…”

Section: Discussionmentioning

confidence: 99%

Planktic Cyanobacteria in the Lower Uruguay River, South America.

Ferrari¹,

Pérez²,

Dabezies³

et al. 2011

Fottea

View full text Add to dashboard Cite

Abstract:The Uruguay River is the second most important river in the Río de la Plata Basin. Taxonomical composition, abundance and distribution of cyanobacteria collected at nine sampling stations in the Lower Uruguay River (Uruguay) were analyzed seasonally from 2006 to 2009. A total of 24 taxa were identified, including 13 Chroococcales, 4 Oscillatoriales and 7 Nostocales species. The genera Dolichospermum and Microcystis presented the highest number of species among the planktic water bloom-forming cyanobacteria. The highest densities of cyanobacteria were recorded in summer during a bloom, with 6.2×10 6 cells.ml -1 , and the most abundant species were Microcystis aeruginosa and Dolichospermum cf. pseudocompactum. In this case, the toxicity analyses by HPLC did not indicate the presence of microcystin-LR. Phytoplankton growth in the Uruguay River was found not to be nutrient-limited. The high correlation of cyanobacteria densities with nitrogen and phosphorous compounds is directly related to changes in flow. Cyanobacteria densities increased with summer high temperatures in low flow conditions. The ANOSIM analysis showed no significant differences between zones and sampling sites, but there were temporal significant differences in relation to seasonal samplings. Radiocystis fernandoi KoMáreK et KoMarKová-Legnerová was recorded for the first time in Uruguay and Dolichospermum cf. pseudocompactum (Watanabe) WacKLin et al. was recorded for the first time in South America.

show abstract

“…The selection of the parameters is based upon their relative significance in determining the growth and movement of cyanobacterial colonies according to the simulation results presented in Guven and Howard [13] and results of previously published studies [2,[17][18][19]38]. The sensitivity ranking of the parameters is given in Table 2 in the order of importance.…”

Section: Discussionmentioning

confidence: 99%

Sensitivity Analysis of a Cyanobacterial Growth and Movement Model under Two Different Flow Regimes

Güven

Howard

2011

Environ Model Assess

Self Cite

View full text Add to dashboard Cite

Bloom-forming and toxin-producing cyanobacteria remain a persistent nuisance across the world. Modelling cyanobacterial behaviour in freshwaters is an important tool for understanding their population dynamics and predicting the location and timing of the bloom events in lakes, reservoirs and rivers. A new deterministicmathematical model was developed, which simulates the growth and movement of cyanobacterial blooms in river systems. The model focuses on the mathematical description of the bloom formation, vertical migration and lateral transport of colonies within river environments by taking into account the major factors that affect the cyanobacterial bloom formation in rivers including light, nutrients and temperature. A parameter sensitivity analysis using a oneat-a-time approach was carried out. There were two objectives of the sensitivity analysis presented in this paper: to identify the key parameters controlling the growth and movement patterns of cyanobacteria and to provide a means for model validation. The result of the analysis suggested that maximum growth rate and day length period were the most significant parameters in determining the population growth and colony depth, respectively.

show abstract

Modelling the growth and movement of cyanobacteria in river systems

Cited by 29 publications

References 21 publications

Status, Alert System, and Prediction of Cyanobacterial Bloom in South Korea

Status, Alert System, and Prediction of Cyanobacterial Bloom in South Korea

Planktic Cyanobacteria in the Lower Uruguay River, South America.

Sensitivity Analysis of a Cyanobacterial Growth and Movement Model under Two Different Flow Regimes

Contact Info

Product

Resources

About