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A non-linear neural network technique for updating of river flow forecasts

Shamseldin, A. Y.
; 
O’Connor, K. M.
Identifiers
http://hdl.handle.net/10379/9867
https://doi.org/10.13025/24570
Repository DOI
10.5194/hess-5-577-2001
Publication Date
2001-01-01
Keywords
auto-regressive exogenous-input model, neural network, output-updating procedure, soil moisture accounting and routing (smar), model, rainfall-runoff models, conceptual catchment models, water-quality parameters, genetic algorithm, calibration, prediction, validation, time, optimization, outputs
Type
Article
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Citation
Shamseldin, A. Y. O’Connor, K. M. (2001). A non-linear neural network technique for updating of river flow forecasts. Hydrology and Earth System Sciences 5 (4), 577-597
Abstract
A non-linear Auto-Regressive Exogenous-input model (NARXM) river flow forecasting output-updating procedure is presented. This updating procedure is based on the structure of a multi-layer neural network. The NARXM-neural network updating procedure is tested using the daily discharge forecasts of the soil moisture accounting and routing (SMAR) conceptual model operating on five catchments having different climatic conditions. The performance of the NARXM-neural network updating procedure is compared with that of the linear Auto-Regressive Exogenous-input (ARXM) model updating procedure, the latter being a generalisation of the widely used Auto-Regressive (AR) model forecast error updating procedure. The results of the comparison indicate that the NARXM procedure performs better than the ARXM procedure.
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Publisher
Copernicus GmbH
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Rights
Attribution-NonCommercial-NoDerivs 3.0 Ireland
cbn
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Externally hosted open access publications with University of Galway authors (1)