Correction of Timing Errors of Artificial Neural Network Rainfall-Runoff Models View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2009

AUTHORS

N.J. de Vos , T.H.M. Rientjes

ABSTRACT

In this study, multi-layer feedforward artificial neural network (ANN) models were developed for forecasting the runoff from the Geer catchment in Belgium. The models produced a good overall approximation of the hydrograph, but the forecasts tended to be plagued by timing errors. These were caused by the use of previous discharge as ANN input, which became dominant and effectively caused lagged forecasts. Therefore, an aggregated objective function was tested that punishes the ANN model for having a timing error. The gradient-based training algorithm that was used had difficulty with finding good optima for this function, but nevertheless some hopeful results were found. There seems to be a trade-off between having good overall fit and having correct timing, so further research is suggested to find balanced ANN models that satisfy both objectives. More... »

PAGES

101-112

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-79881-1_8

DOI

http://dx.doi.org/10.1007/978-3-540-79881-1_8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025347443


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