288
H. Wilson . F. Recknagel
Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias/variance
dilemma. Neural Computation 4:1-58
Harris GP (1986) Phytoplankton ecology : structure, function and fluctuation. Chapman and
Hall, London
Karnl C, Soyupak S, Cilesiz A, Akbay N, Germen E (2000) Case studies on the use of
neural networks in eutrophication modelling. Ecological Modelling 134:145-152
Lawrence S, Giles CL (2000) Overfitting and Neural Networks: Conjugate Gradient and
Backpropagation. Proceedings of the International Joint Conference on Neural
Networks, Corno, Italy.IEEE Computer Society, Los Alamitos, CA. 114-119
Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water
resources variables: a review of modelling issues and applications. Environmental
Modelling and Software 15:101-124
Maier HR, Dandy GC, Burch MD (1998) Use of artificial neural networks for modelling
cyanobacteria Anabaena spp. in the River Murray, South Australia. Ecological
Modelling 105:257-272
M(Illler MF (1993) A scaled conjugate gradient algorithm for fast supervised leaming.
Neural Networks 6:525-533
Moody JE (1991) Note on generalization, regularization, and architecture selection in
nonlinear leaming systems. In Juang BH, Kung SY, Kamm CA (eds) Workshop on
Neural Networks for Signal Processing, IEEE Signal Processing Society, Princeton, NJ
1-10
National Rivers Authority (1990) Toxic Blue-green Algae. NRA Water Quality Series
Report No 2, Bristol, 128pp
Parliament of the Commonwealth of Australia (1993) Water resources, toxic algae. In
Senate Standing Committee on Environment Recreation and the Arts, Technical report
Recknagel F, French M, Harkonen P, Yabunaka K (1997) Artificial neural network
approach for modelling and prediction of algal blooms. Ecological Modelling 96 (1-3)
11-28
Recknagel F, Fukushima T, Hanazato T, Takaumura N, Wilson H (1998) Modelling and
prediction of phyto- and zooplankton dynamics in Lake Kasumigaura by artificial
neural networks. Lakes & Reservoirs 3 (2): 123-133
Recknagel F, Wilson H (2000) Elucidation and prediction of aquatic ecosystems by
artificial neuronal networks. In Lek S, Guegan J (eds) Artificial neuronal networks
application to ecology and evolution. Springer, Berlin, 143-155
Scardi M (1996)
Artificial neural networks as empirical models for estimating
phytoplankton production. Marine Ecology Progress Series 139:289-299
Smith M (1993) Neural networks for statistical modeling. Van Nostrand, New York
Takamura N, Aizaki M (1991) Change in primary production in Lake Kasumigaura (19861989) accompanied by transition of dominant species. Jpn J Limnol 52 (3): 173-187
Vollenweider RA (1970) Scientific fundamentals ofthe eutrophication oflakes and flowing
waters, with particular reference to nitrogen and phosphorous as factors in
eutrophication. Technical Report DASISST 68.27, Organisation for Economic
Cooperation and Development
Waibel A (1989) Modular construction of time-delay neural networks for speech
recognition. Neural Computation 1:39-46
Wasserman PD (1989) Neural computing: theory and practice. Van Nostrand Reinhold,
New York
H. Wilson . F. Recknagel
Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias/variance
dilemma. Neural Computation 4:1-58
Harris GP (1986) Phytoplankton ecology : structure, function and fluctuation. Chapman and
Hall, London
Karnl C, Soyupak S, Cilesiz A, Akbay N, Germen E (2000) Case studies on the use of
neural networks in eutrophication modelling. Ecological Modelling 134:145-152
Lawrence S, Giles CL (2000) Overfitting and Neural Networks: Conjugate Gradient and
Backpropagation. Proceedings of the International Joint Conference on Neural
Networks, Corno, Italy.IEEE Computer Society, Los Alamitos, CA. 114-119
Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water
resources variables: a review of modelling issues and applications. Environmental
Modelling and Software 15:101-124
Maier HR, Dandy GC, Burch MD (1998) Use of artificial neural networks for modelling
cyanobacteria Anabaena spp. in the River Murray, South Australia. Ecological
Modelling 105:257-272
M(Illler MF (1993) A scaled conjugate gradient algorithm for fast supervised leaming.
Neural Networks 6:525-533
Moody JE (1991) Note on generalization, regularization, and architecture selection in
nonlinear leaming systems. In Juang BH, Kung SY, Kamm CA (eds) Workshop on
Neural Networks for Signal Processing, IEEE Signal Processing Society, Princeton, NJ
1-10
National Rivers Authority (1990) Toxic Blue-green Algae. NRA Water Quality Series
Report No 2, Bristol, 128pp
Parliament of the Commonwealth of Australia (1993) Water resources, toxic algae. In
Senate Standing Committee on Environment Recreation and the Arts, Technical report
Recknagel F, French M, Harkonen P, Yabunaka K (1997) Artificial neural network
approach for modelling and prediction of algal blooms. Ecological Modelling 96 (1-3)
11-28
Recknagel F, Fukushima T, Hanazato T, Takaumura N, Wilson H (1998) Modelling and
prediction of phyto- and zooplankton dynamics in Lake Kasumigaura by artificial
neural networks. Lakes & Reservoirs 3 (2): 123-133
Recknagel F, Wilson H (2000) Elucidation and prediction of aquatic ecosystems by
artificial neuronal networks. In Lek S, Guegan J (eds) Artificial neuronal networks
application to ecology and evolution. Springer, Berlin, 143-155
Scardi M (1996)
Artificial neural networks as empirical models for estimating
phytoplankton production. Marine Ecology Progress Series 139:289-299
Smith M (1993) Neural networks for statistical modeling. Van Nostrand, New York
Takamura N, Aizaki M (1991) Change in primary production in Lake Kasumigaura (19861989) accompanied by transition of dominant species. Jpn J Limnol 52 (3): 173-187
Vollenweider RA (1970) Scientific fundamentals ofthe eutrophication oflakes and flowing
waters, with particular reference to nitrogen and phosphorous as factors in
eutrophication. Technical Report DASISST 68.27, Organisation for Economic
Cooperation and Development
Waibel A (1989) Modular construction of time-delay neural networks for speech
recognition. Neural Computation 1:39-46
Wasserman PD (1989) Neural computing: theory and practice. Van Nostrand Reinhold,
New York