Artificial neural network approach for modelling and prediction of algal blooms

Following a comparison of current alternative approaches for modelling and prediction of algal blooms, artificial neural networks are introduced and applied as a new, promising model type. The neural network applications were developed and validated by limnological time-series from four different freshwater systems. The water-specific time-series comprised cell numbers or biomass of the ten dominating algae species as observed over up to twelve years and the measured environmental driving variables. The resulting predictions on succession, timing and magnitudes of algal species indicate that artificial neural networks can fit the complexity and nonlinearity of ecological phenomena apparently to a high degree.