A comparison of artificial neural networks used for river forecasting

Abstract

Abstract. This paper compares the performance of two artificial neural network (ANN) models – the multi layer perceptron (MLP) and the radial basis function network (RBF) – with a stepwise multiple linear regression model (SWMLR) and zero order forecasts (ZOF) of river flow. All models were trained using 15 minute rainfall-runoff data for the River Mole, a flood-prone tributary of the River Thames, UK. The models were then used to forecast river flows with a 6 hour lead time and 15 minute resolution, given only antecedent rainfall and discharge measurements. Two seasons (winter and spring) were selected for model testing using a cross-validation technique and a range of diagnostic statistics. Overall, the MLP was more skillful than the RBF, SWMLR and ZOF models. However, the RBF flow forecasts were only marginally better than those of the simpler SWMLR and ZOF models. The results compare favourably with a review of previous studies and further endorse claims that ANNs are well suited to rainfall-runoff modelling and (potentially) real-time flood forecasting.