Prediction of cyanobacterial blooms in the Dau Tieng Reservoir using an artificial neural network

An artificial neural network (ANN) model was used to predict the cyanobacteria bloom in the Dau Tieng Reservoir, Vietnam. Eight environmental parameters (pH, dissolved oxygen, temperature, total dissolved solids, total nitrogen (TN), total phosphorus, biochemical oxygen demand and chemical oxygen demand) were introduced as inputs, whereas the cell density of three cyanobacteria genera (Anabaena, Microcystis and Oscillatoria) with microcystin concentrations were introduced as outputs of the three-layer feed-forward back-propagation ANN. Eighty networks covering all combinations of four learning algorithms (Bayesian regularisation (BR), gradient descent with momentum and adaptive learning rate, Levenberg-Mardquart, scaled conjugate gradient) with two transfer functions (tansig, logsig) and 10 numbers of hidden neurons (6-16) were trained and validated to find the best configuration fitting the observed data. The result is a network using the BR learning algorithm, tansig transfer function and nine neurons in the hidden layer, which shows satisfactory predictions with the low values of error (root mean square error = 0.108) and high correlation coefficient values (R = 0.904) between experimental and predicted values. Sensitivity analysis on the developed ANN indicated that TN and temperature had the most positive and negative effects respectively on microcystin concentrations. These results indicate that ANN modelling can effectively predict the behaviour of the cyanobacteria bloom process.