Water in Celtic Countries: Quantity, Quality and Climate Variability (Proceedings of the Fourth InterCeltic Colloquium on Hydrology and Management of Water Resources, Guimarăes, Portugal, July 2005). IAHS Publ. 310, 2007, 267-276.
Application of Artificial
Neural Networks for river flow simulation in three French catchments
MONOMOY GOSWAMI & KIERAN M.
O’CONNOR
Department of Engineering Hydrology,
National University of Ireland, Galway, Ireland
monomoy.goswami@nuigalway.ie
Abstract For
more than a decade, Artificial Neural Networks (ANNs) have been increasingly
used in hydrology as flexible black-box models of non-linear type. Within this
category of models, the “multi-layer feed-forward network” used in this study
consists of an input layer, an output layer, and one “hidden” layer in between.
The model is applied to daily data of three catchments, all located in
northwest France, for river flow simulation and forecasting and its performance
is compared with those of five system-theoretic models and one conceptual
model. The ANN is observed to be the best performing individual model for the
catchments tested. In the subsequent application of the Neural Network Method
(NNM) for combining the outputs of the individual models, in different
combinations, i.e. in a “multi-model approach” for deriving consensus
forecasts, the NNM (as one of three Model Output Combination Techniques (MOCTs)
considered) is found to be the best performing MOCT and also better than the
best individual model. The Galway Flow Modelling and Forecasting System
(GFMFS), a software package developed by the present authors, is used in the
study.
Key
words
black-box models; hidden layer; multi-model approach; Neural Networks