183
Views
4
CrossRef citations to date
0
Altmetric
Original Articles

Modeling Phytoplankton Dynamics in the River Darling (Australia) Using the Radial Basis Function Neural Network

Pages 639-647 | Received 10 Feb 2006, Accepted 05 Jun 2006, Published online: 06 Jan 2011

LITERATURE CITED

  • Bianchini , M. , Frasconi , P. and Gori , M. 1995 . Learning without local minima in radial basis function networks . IEEE Trans. Neural Networks , 6 : 749 – 756 .
  • Broomhead , D. S. and Lowe , D. 1988 . Multi-variable functional interpolation and adaptive networks . Complex Syst. , 2 : 321 – 355 .
  • Chen , S. , Cowan , C. F.N. and Grant , P. M. 1991 . Orthogonal least squares learning algorithm for radial basis function networks . IEEE Transactions on Neural Networks , 2 : 302 – 309 .
  • Dibike , Y. B. and Solomatlne , D. P. 2001 . River flow forecasting using artificial neural networks . Phys. Chem. Earth (B) , 26 : 1 – 7 .
  • Ha , K. , Cho , E. A. , Kim , H. W. and Joo , G. J. 1999 . Microcystis bloom formation in the lower Nokdong River, South Korea: importance of hydordynamics and nutrient loading . Freshwater Res. , 50 : 89 – 94 .
  • Harris , G. P. 1986 . Plankton Ecology—Stucture, function, and fluctuation. Chapman and Hall, New York, p.384.
  • Haykin , S. 1999 . Neural networks: a comprehensive foundation New Jersey : Prentice Hall . 2nd edition
  • Hou Guoxiang , Lirong Song , Liu , Jiantong , Xiao , Bangding and Liu , Yongding . 2004 . Modeling of cyanobacterial blooms in hypereutrophic Lake Dianchi . China. J. Freshwater Ecol. , 19 : 623 – 629 .
  • Jeong , K. S. , Recknagel , F. and Joo , G. J. 2005 . “ Prediction and elucidation of population dynamics of the blue-green algae Microcystis aeruginosa and the diatom Stephanodiscus hantzschii in the Nakdong River-Reservior systems (South Korea) by a recurrent artificial neural network. ” . In Ecological informatics Edited by: Recknagel , F. 255 – 273 . New York : Springer-Verlag . 2nd edition
  • Jeong , K. S. , Joo , G. J. , Kim , H. W. , Ha , K. and Recknagel , F. 2001 . Prediction and elucidation of phytoplankton dynamics in the Nakdong River (Korea) by means of a recurrent neural network . Ecological Modelling , 146 : 115 – 129 .
  • Kromkamp , J. and Walsby , A. E. 1990 . A computer model of buoyancy and vertical migration in cyanobacteria . J. Plankton Res. , 12 : 161 – 183 .
  • Liu , Y. , Han , M. , Liang , Z. and Lin , Y. 1995 . Influence of light intensity, temperature and nutrients on the growth of Microcystis in water of Dianchi Lake . Res. Environ. Sci. , 8 : 7 – 11 .
  • Maier , H. R. , Dandy , G. C. and Burch , M. D. 1998 . Use of artificial neural networks for modeling cyanobacterial Anabaena spp. in the River Murray . South Australia. Ecological Modelling , 105 : 257 – 272 .
  • Maier , H. R. and Dandy , G. C. 1997 . Modelling cyanobacterial (blue-green algae) in the River Murray using artificial neural networks . Mathematics and Computers in Simulation , 43 : 377 – 386 .
  • Maier , H. R. and Dandy , G. C. 2001 . Neural network based modeling of environmental variables: a system approach . Mathematical and Computer Modeling , 33 : 669 – 682 .
  • Moody , J. and Darken , C. 1989 . Faster learning in networks of locally tuned processing units . Neural Computing , 1 : 281 – 294 .
  • Morelli , M. J. , Montagna , G. , Nicrosini , O. , Treccani , M. , Farina , M. and Amato , P. 2004 . Pricing financial derivatives with neural networks . Physica A , 338 : 160 – 165 .
  • Moss , B. 1998 . Ecology of fresh waters: man and medium 557 Oxford : Blackwell Science . past to future, 3rd edition
  • Okada , M. and Aiba , S. 1983 . Simulation of water blooms in a eutrophic lake: Modeling the vertical migration in a population of Microsystis aeruginosa. . Wat. Res. , 20 : 485 – 490 .
  • Park , J. and Sandberg , I. W. 1991 . Universal approximation using radial basis functions network . Neural Computing , 3 : 246 – 257 .
  • Rechnagel , Friedrich . 1997 . ANNA—Artificial neural network model for predicting species abundance and succession of blue-green algae . Hydrobiologia , 349 : 47 – 57 .
  • Reynolds , C. S. 1984 . The ecology of freshwater phytoplankton 384 Cambridge : Cambridge University Press .
  • Reynolds , C. S. 1992 . The river handbook: hydrological and ecological principles, Vol. 1. 526 Oxford : Blackwell Scientific Publication .
  • Rivas , V. M. , Merelo , J. J. , Castillo , P. A. , Arenas , M. G. and Castellano , J. G. 2004 . Evolving RBF neural networks for time-series forecasting with EvRBF . Information Sciences , 165 : 207 – 220 .
  • Shapiro , J. 1973 . Blue-green algae: why they become dominant . Science , 179 : 382 – 384 .
  • Shapiro , J. 1990 . Current beliefs regarding dominance by blue-greens: the case for the importance of CO2 and pH . Verh. Int. Verein. Limnol. , 24 : 38 – 54 .
  • Sheta , A. F. and de Jong , K. 2001 . Time-series forecasting using GA-tuned radial basis functions . Information Sciences , 133 ( 3–4 ) : 221 – 228 .
  • Sommer , U. , Gliwica , Z. M. , Lampert , W. and Duncan , A. 1986 . The PEG-model of seasonal succession of planktonic events in fresh waters . Arch. Hydrobiol. , 106 : 443 – 471 .
  • Suttle , C. A. and Harrison , P. J. 1988 . Ammonium and phosphate uptake rates, N: P supply ratios, and evidence for N 2nd P limitation in some oligotrophic lakes . Limnol. Oceanogr. , 33 : 186 – 202 .
  • Walter , M. , Recknagel , F. , Carpenter , C. and Bormans , M. 2001 . Predicting eutrophication effects in the Burrenjuck Reservoir (Australia) by means of the deterministic model SALMO and the recurrent neural network model ANNA . Ecological Modelling , 146 : 97 – 113 .
  • Wei Bin , Norio Sugiura and Maekawa , Takaaki . 2001 . Use of artificial neural network in the prediction of algal blooms . Water Resource , 35 : 2022 – 2028 .
  • Yabunaka Ken-ichi , Masaaki Hosomi and Murakami , Akihiko . 1997 . Novel application of a back-propagation artificial neural network model formulated to predict algal bloom . Wat. Sci. Tech. , 36 : 89 – 97 .
  • Yao Xiaojun , Xiaoyun Zhang , Zhang , Ruisheng , Liu , Mancang , Hu , Zhide and Fan , Botao . 2001 . Prediction of enthalpy of alkanes by the use of radial basis function neural networks . Computers and Chemistry , 25 : 475 – 482 .
  • Zar , J. H. 1984 . Biostatistical analysis 718 Prentice-Hall, New Jersey 2nd edition

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.