310
J. Bobbin . F. Recknagel
ACknowledgements
The authors would like to thank the anonymous referees for their contribution
towards improvements in this paper, and Takehiko Fukushima, Noriko Takamura
and Takayuki Hanazato of the National Institute of Environmental Studies in
Tsukuba, Japan, for providing data of the Lake Kasumigaura.
References
Baeck, T (1996) Evolutionary Algorithms in Theory and Practice. Oxford
University Press, New York
Barciella RM, Garcia E, Fernandez E (1999) Modelling primary productivity in a
coastal embayment affected by upwelling using dynamic ecosystem models
and artificial neural networks. Ecol. Modelling 120 (2-3) 199-212.
Bobbin J, Recknagel F (2001) Knowledge Discovery for Prediction and
Explanation of Blue-Green Algal Dynamics in Lakes by Evolutionary
Algorithms. Ecol. Modelling 146, 1-3,253-264
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and
Regression Trees. StatisticallProbability Series. Wadsworth International
Group, New York
Chon TS, Kwak IS, Park YS, Kim TH, Kim YS 2001) Patterning and short-term
predictions of benthic macroinvertebrate community dynamics by using a
recurrent artificial neural network. Ecol. Modelling 146, 181-193.
Ghozeil A, Fogel DB (1996) Discovering patterns in spatial data using
evolutionary programming. In: Koza, JR, Goldberg, DE, Fogei, DB, Riolo,
RL (eds) (1996) Genetic Programming. MIT Press, Cambridge, MA, 521-527
Harris GP (1986) Plankton Ecology - Structure, Function, and Fluctuation.
Chapman and Hall, New York
Jeong KS, Joo GJ, Kim HW, Ha K, Recknagel, F (2001) Prediction and
elucidation of phytoplankton dynamics in the Nakdong River (Korea) by
means of a recurrent artificial neural bnetwork. Ecol. Modelling 146, 1-3,
115-119.
Recknagel F, Wilson H (2000) Elucidation and prediction of aquatic ecosystems
by artificial neural netwarks. In: Lek S, Guegan JF (eds.) Artificial Neuronal
networks in Ecology and Evolution. Springer-Verlag, New York, 143-155
Recknagel F, Bobbin J, Whigham P, Wilson H (2002) Comparative application of
artificial neural networks and genetic algorithms for multivariate time series
modelling of algal blooms in freshwater lakes. Journal of Hydroinformatics (in
press)
Recknagel F, French M, Harkonen P, Yabunaka KI (1997) Artificial neural
network approach far modelling and prediction of algal blooms. Ecol.
Modelling 96, 1-3, 11-28
Reynolds CS (1984) The Ecology ofFreshwater Phytoplankton. Cambridge
University Press, New York
Schwefel HP (1995) Evolution and Optimum Seeking. John Wiley and Sons, New
J. Bobbin . F. Recknagel
ACknowledgements
The authors would like to thank the anonymous referees for their contribution
towards improvements in this paper, and Takehiko Fukushima, Noriko Takamura
and Takayuki Hanazato of the National Institute of Environmental Studies in
Tsukuba, Japan, for providing data of the Lake Kasumigaura.
References
Baeck, T (1996) Evolutionary Algorithms in Theory and Practice. Oxford
University Press, New York
Barciella RM, Garcia E, Fernandez E (1999) Modelling primary productivity in a
coastal embayment affected by upwelling using dynamic ecosystem models
and artificial neural networks. Ecol. Modelling 120 (2-3) 199-212.
Bobbin J, Recknagel F (2001) Knowledge Discovery for Prediction and
Explanation of Blue-Green Algal Dynamics in Lakes by Evolutionary
Algorithms. Ecol. Modelling 146, 1-3,253-264
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and
Regression Trees. StatisticallProbability Series. Wadsworth International
Group, New York
Chon TS, Kwak IS, Park YS, Kim TH, Kim YS 2001) Patterning and short-term
predictions of benthic macroinvertebrate community dynamics by using a
recurrent artificial neural network. Ecol. Modelling 146, 181-193.
Ghozeil A, Fogel DB (1996) Discovering patterns in spatial data using
evolutionary programming. In: Koza, JR, Goldberg, DE, Fogei, DB, Riolo,
RL (eds) (1996) Genetic Programming. MIT Press, Cambridge, MA, 521-527
Harris GP (1986) Plankton Ecology - Structure, Function, and Fluctuation.
Chapman and Hall, New York
Jeong KS, Joo GJ, Kim HW, Ha K, Recknagel, F (2001) Prediction and
elucidation of phytoplankton dynamics in the Nakdong River (Korea) by
means of a recurrent artificial neural bnetwork. Ecol. Modelling 146, 1-3,
115-119.
Recknagel F, Wilson H (2000) Elucidation and prediction of aquatic ecosystems
by artificial neural netwarks. In: Lek S, Guegan JF (eds.) Artificial Neuronal
networks in Ecology and Evolution. Springer-Verlag, New York, 143-155
Recknagel F, Bobbin J, Whigham P, Wilson H (2002) Comparative application of
artificial neural networks and genetic algorithms for multivariate time series
modelling of algal blooms in freshwater lakes. Journal of Hydroinformatics (in
press)
Recknagel F, French M, Harkonen P, Yabunaka KI (1997) Artificial neural
network approach far modelling and prediction of algal blooms. Ecol.
Modelling 96, 1-3, 11-28
Reynolds CS (1984) The Ecology ofFreshwater Phytoplankton. Cambridge
University Press, New York
Schwefel HP (1995) Evolution and Optimum Seeking. John Wiley and Sons, New