Modeling of algal blooms in freshwaters using artificial neural networks

Abstract

The development of a neural network model for predicting algal blooms is described. The neural network consists of a three layer structure with input, hidden, and output layers. Training is conducted using back-propagation where the data are presented as a series of learning sets such that the inputs are observable water quality parameters and outputs are the biomass quantities of specific algal groups. Training is conducted using 3 yr of daily values of water quality parameters and validation is performed using 2 yr of independent daily values. The neural network is shown to perform well for predicting both the timing and magnitude of algae blooms for data in use in training set and to accurately predict the timing and typically over- or under-estimate the magnitude of blooms when applied to the independent data. -from Authors