• neural network;
  • multilayer feedforward network;
  • multivariate function;
  • tidal river


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 & Sons, Ltd.