Author(s): J C Mason; R.K. Price; A. Tem'Me

Linked Author(s):

Keywords:

Abstract: In modelling rainfall-runoff and flows in drainage systems it can be advantageous to adopt a neural network (NN). Unfortunately traditional NN learning procedures such as back-propagation can be very slow and expensive to carry out. However, if radial basis function (RBF) networks are adopted with radial centres fixed by a suitable data clustering technique then good results may be obtained very much more rapidly. RBF networks are here shown to be very effective in modelling runoff for a large rainfall database and to give broadly comparable results to those obtained by fine-tuning the much slower back-propagation procedure. The specific model is based on the assumption that runoff depends on time, rainfall intensity I, the rate of change of I and the integral of I.

DOI: https://doi.org/10.1080/00221689609498476

Year: 1996