Towards a generic artificial neural network model for dynamic predictions of algal abundance in freshwater lakes

A generalised architecture of a feedforward ANN for the prediction of algal abundance is suggested. It simplifies practical model applications, rationalises data collection and preprocessing, improves model validity, and enables meaningful comparison of ANN models between lakes. The generic ANN model considers the key driving variables of algal growth such as phosphorous, nitrogen, underwater light and water temperature as input nodes and predicts algal species abundance or biomass as output. Two model structures were used; one for same-day and one for 30-days ahead predictions of algal abundance. ANN models with and without hidden layers were compared to determine the impact of the addition of non-linear processing capabilities on model performance. A bootstrap aggregation method was found to reduce test set prediction error and to mitigate the effects of overfitting. The model was validated by means of time-series data from six different freshwater lakes.