Identification of appropriate low flow forecast model for the Meuse River.

M.C. Demirel; Martijn J. Booij

Identification of appropriate low flow forecast model for the Meuse River.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

This study investigates the selection of an appropriate low flow forecast model for the Meuse River based on the comparison of output uncertainties of different models. For this purpose, three data driven models have been developed for the Meuse River: a multivariate ARMAX model, a linear regression model and an Artificial Neural Network (ANN) model. The uncertainty in these three models is assumed to be represented by the difference between observed and simulated discharge. The results show that the ANN low flow forecast model with one or two input variables(s) performed slightly better than the other statistical models when forecasting low flows for a lead time of seven days. The approach for the selection of an appropriate low flow forecast model adopted in this study can be used for other lead times and river basins as well.

Original language	Undefined
Title of host publication	Proceedings of the Joint IAHS & IAH International Convention, Hyderabad, 6-12 September 2009
Editors	Ian Cluckie, Yangbo Chen, Vladan Babovic, Lenny Konikow, Arthur Mynett, Siegfried Demuth, Dragan A. Savic
Place of Publication	Hyderabad
Publisher	IAHS publications
Pages	296-303
Number of pages	528
ISBN (Print)	9781907161025
Publication status	Published - 6 Sep 2009
Event	Symposium JS.4 at the - Hyderabad, India Duration: 1 Sep 2009 → 1 Sep 2009

Publication series

Name	331
Publisher	IAHS Press
Number	331
ISSN (Print)	0144-7815

Conference

Conference	Symposium JS.4 at the
Period	1/09/09 → 1/09/09
Other	September 2009

Keywords

linear regression model
METIS-259105
Uncertainty
Meuse River
Low flows
ARMAX
ANN
Appropriate model
IR-78691

Access to Document

identification_of

http://iahs.info/redbooks/a331/iahs_331_0296.pdf

Cite this

Demirel, M. C., & Booij, M. J. (2009). Identification of appropriate low flow forecast model for the Meuse River. In I. Cluckie, Y. Chen, V. Babovic, L. Konikow, A. Mynett, S. Demuth, & D. A. Savic (Eds.), Proceedings of the Joint IAHS & IAH International Convention, Hyderabad, 6-12 September 2009 (pp. 296-303). (331; No. 331). IAHS publications. http://iahs.info/redbooks/a331/iahs_331_0296.pdf

Demirel, M.C. ; Booij, Martijn J. / Identification of appropriate low flow forecast model for the Meuse River. Proceedings of the Joint IAHS & IAH International Convention, Hyderabad, 6-12 September 2009. editor / Ian Cluckie ; Yangbo Chen ; Vladan Babovic ; Lenny Konikow ; Arthur Mynett ; Siegfried Demuth ; Dragan A. Savic. Hyderabad : IAHS publications, 2009. pp. 296-303 (331; 331).

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abstract = "This study investigates the selection of an appropriate low flow forecast model for the Meuse River based on the comparison of output uncertainties of different models. For this purpose, three data driven models have been developed for the Meuse River: a multivariate ARMAX model, a linear regression model and an Artificial Neural Network (ANN) model. The uncertainty in these three models is assumed to be represented by the difference between observed and simulated discharge. The results show that the ANN low flow forecast model with one or two input variables(s) performed slightly better than the other statistical models when forecasting low flows for a lead time of seven days. The approach for the selection of an appropriate low flow forecast model adopted in this study can be used for other lead times and river basins as well.",

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Demirel, MC & Booij, MJ 2009, Identification of appropriate low flow forecast model for the Meuse River. in I Cluckie, Y Chen, V Babovic, L Konikow, A Mynett, S Demuth & DA Savic (eds), Proceedings of the Joint IAHS & IAH International Convention, Hyderabad, 6-12 September 2009. 331, no. 331, IAHS publications, Hyderabad, pp. 296-303, Symposium JS.4 at the, 1/09/09. <http://iahs.info/redbooks/a331/iahs_331_0296.pdf>

Identification of appropriate low flow forecast model for the Meuse River. / Demirel, M.C.; Booij, Martijn J.

Proceedings of the Joint IAHS & IAH International Convention, Hyderabad, 6-12 September 2009. ed. / Ian Cluckie; Yangbo Chen; Vladan Babovic; Lenny Konikow; Arthur Mynett; Siegfried Demuth; Dragan A. Savic. Hyderabad : IAHS publications, 2009. p. 296-303 (331; No. 331).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - This study investigates the selection of an appropriate low flow forecast model for the Meuse River based on the comparison of output uncertainties of different models. For this purpose, three data driven models have been developed for the Meuse River: a multivariate ARMAX model, a linear regression model and an Artificial Neural Network (ANN) model. The uncertainty in these three models is assumed to be represented by the difference between observed and simulated discharge. The results show that the ANN low flow forecast model with one or two input variables(s) performed slightly better than the other statistical models when forecasting low flows for a lead time of seven days. The approach for the selection of an appropriate low flow forecast model adopted in this study can be used for other lead times and river basins as well.

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