Application of a neural network technique to rainfall-runoff modelling

This paper deals with the application of a neural network technique in the context of rainfall-runoff modelling. The chosen form of neural network is tested using different types of input information, namely, rainfall, historical seasonal and nearest neighbour information. Using the data of six catchments, the technique is applied for four different input scenarios in each of which some or all of these input types are used. The performance of the technique is compared with those of models that utilize similar input information, namely, the simple linear model(SLM), the seasonally based linear perturbation model (LPM) and the nearest neighbour linear perturbation model (NNLPM). The results suggest that the neural network shows considerable promise in the context of rainfall-runoff modelling but, like all such models, has variable results. (C) 1997 Elsevier Science B.V.