Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments

Abstract

The forecasting power of neural network (NN) and autoregressive moving average (ARMA) models are compared. Modelling experiments were based on a 3-year period of continuous river flow data for two contrasting catchments: the Upper River Wye and the River Ouse. Model performance was assessed using global and storm-specific quantitative evaluation procedures. The NN and ARMA solutions provided similar results, although naive predictions yielded poorer estimates. The annual data were then grouped into a set of distinct hydrological event types using a self-organizing map and two rising event clusters were modelled using the NN technique. These alternative investigations provided encouraging results. Copyright © 2000 John Wiley & Sons, Ltd.