Year: 1996
Type: article
Abstract: A series of numerical experiments, in which flow data were generated from synthetic storm sequences routed through a conceptual hydrological model consisting of a single nonlinear reservoir, has demonstrated the closeness of fit that can be achieved to such data sets using Artificial Neural Networks (ANNs). The application of different standardization factors to both training and verification sequences has underlined the importance of such factors to network performance. Trials with both one and two hidden layers in the ANN have shown that, although improved performances are achieved with the extra hidden layer, the additional computational effort does not appear justified for data sets exhibiting the degree of nonlinear behaviour typical of rainfall and flow sequences from many catchment areas.
Source: Hydrological Sciences Journal
Authors: A. W. MINNS, M. J. HALL
Institution: IHE Delft Institute for Water Education
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2011412119