Developing an empirical model of phytoplankton primary production: a neural network case study

Developing an empirical model of phytoplankton primary production: a neural network case study We describe the development of a neural network model for estimating primary production of phytoplankton. Data from an enriched estuary in the eastern United States, Chesapeake Bay, were used to train, validate and test the model. Two error backpropagation multilayer perceptrons were trained: a simpler one (3-5-1) and a more complex one (12-5-1). Both neural networks outperformed conventional empirical models, even though only the latter, which exploits a larger suite of predictive variables, provided truly accurate outputs. The application of this neural network model is thoroughly discussed and the results of a sensitivity analysis are also presented. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

Developing an empirical model of phytoplankton primary production: a neural network case study

Ecological Modelling, Volume 120 (2) – Aug 17, 1999

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Publisher
Elsevier
Copyright
Copyright © 1999 Elsevier Science B.V.
ISSN
0304-3800
eISSN
1872-7026
D.O.I.
10.1016/S0304-3800(99)00103-9
Publisher site
See Article on Publisher Site

Abstract

We describe the development of a neural network model for estimating primary production of phytoplankton. Data from an enriched estuary in the eastern United States, Chesapeake Bay, were used to train, validate and test the model. Two error backpropagation multilayer perceptrons were trained: a simpler one (3-5-1) and a more complex one (12-5-1). Both neural networks outperformed conventional empirical models, even though only the latter, which exploits a larger suite of predictive variables, provided truly accurate outputs. The application of this neural network model is thoroughly discussed and the results of a sensitivity analysis are also presented.

Journal

Ecological ModellingElsevier

Published: Aug 17, 1999

References

  • Application of neural networks to modelling nonlinear relationships in ecology
    Lek, S.; Delacoste, M.; Baran, P.; Dimopoulos, I.; Lauga, J.; Aulagnier, S.
  • Artificial neural network approach for modelling and prediction of algal blooms
    Recknagel, F.; French, M.; Harkonen, P.; Yabunaka, K.I.

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