Application of a neural network model in establishing a stage-discharge relationship for a tidal river

Abstract

This paper presents the applicability of neural network (NN) modelling for forecasting and filtering problems. The multilayer feedforward (MLFF) network was first constructed to forecast the tidal-level variations at the mouth of the River Chao Phraya in Thailand. Unlike the well-known conventional harmonic analysis, the NN model uses a set of previous data for learning and then forecasting directly the time-series of tidal levels. It was found that lead time of 1 to 24 hourly tidal levels can be predicted successfully using only a short-time hourly learning data. The MLFF network was further used to establish a stage-discharge relationship for the tidal river. The results show a considerably better performance of the NN model over the conventional models. In addition, the stage-discharge relationship obtained by the NN model can indicate reasonably well the important behaviour of the tidal influences. Copyright © 2003 John Wiley and Sons, Ltd.