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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...
Neural networks are powerful tools for phytoplankton primary production modeling, even though their application might be hindered by the limited amount of available data. Some new approaches that could enhance neural network models to overcome this problem are presented and discussed in this paper. For instance, co-predictors allow to improve neural network estimates when additional inputs from a...
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