Nonlinear prediction of river water-stages by feedback artificial neural network

The feedback artificial neural network model (FBANNM) was applied to the pr ediction of the water-stages in a tidal river. The difference between a fee d forward artificial neural network model and a FBANNM was investigated. A simple genetic algorithm (SGA) was then incorporated into a FBANNM to help search for the optimal network structure, especially the unit numbers of an input layer and a hidden layer. It was concluded that the FBANNM was a use ful tool in the short-term prediction of the water-stages that had a strong autocorrelation due to tidal motion. The optimal network structure of the FBANNM was effectively determined by the SGA incorporating the fitness defi ned by Akaike's Information Criterion.