[1] Martins Y.Otache, Li Zhijia,. Complementary system-theoretic modelling approachfor enhancing hydrological forecasting [J]. Journal of Southeast University (English Edition), 2006, 22 (2): 273-280. [doi:10.3969/j.issn.1003-7985.2006.02.027]

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Complementary system-theoretic modelling approachfor enhancing hydrological forecasting()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

22

Issue:

2006 2

Page:

273-280

Research Field:

Other Disciplines

Publishing date:

2006-06-30

Info

Title:

Complementary system-theoretic modelling approachfor enhancing hydrological forecasting

Author(s):

Martins Y.Otache;  Li Zhijia

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China

Keywords:

hydrological forecasting;  complementary model;  residual;  Xin’anjiang conceptual model;  artificial neural network

PACS:

P338.9

DOI:

10.3969/j.issn.1003-7985.2006.02.027

Abstract:

Hydrologic models generally represent the most dominant processes since they are mere simplifications of physical reality and thus are subject to many significant uncertainties.As such, a coupling strategy is proposed.To this end, the coupling of the artificial neural network(ANN)with the Xin’anjiang conceptual model with a view to enhance the quality of its flow forecast is presented.The approach uses the latest observations and residuals in runoff/discharge forecasts from the Xin’anjiang model.The two complementary models(Xin’anjiang & ANN)are used in such a way that residuals of the Xin’anjiang model are forecasted by a neural network model so that flow forecasts can be improved as new observations come in.For the complementary neural network, the input data were presented in a patterned format to conform to the calibration regime of the Xin’anjiang conceptual model, using differing variants of the neural network scheme.The results show that there is a substantial improvement in the accuracy of the forecasts when the complementary model was operated on top of the Xin’anjiang conceptual model as compared with the results of the Xin’anjiang model alone.

References:

[1] Abebe A J, Price R K.Enhancing flood forecasts via complementary modeling[A].In:Flood Defence Conference[C].New York:Science Press, 2002.853-859.
[2] Zhao R J, Zhuang Y L, Fang L R, et al.The Xin’anjiang model in hydrological forecasting [A].In:Proceedings of the Oxford Symposium [C].IAHS Publication, 1980, 129:351-356.
[3] Hapuarachchi A P.Improved calibration of conceptual hydrologic models via GIS aided approach [D].Nanjing:Department of Hydrology and Water Resources of Hohai University, 2003.73.
[4] Duan Q, Sorooshian S, Gupta V K.Optimal use of SCE-UA global optimization method for calibrating watershed methods [J].J Hydrol, 1994, 158(3, 4):265-284.
[5] Hsu K L, Gupta V H, Sorooshian S.Artificial neural network modelling of the rainfall-runoff process [J].Water Resources Res, 1995, 31(10):2517-2530.
[6] Nash J E, Sutcliffe J V.Riverflow forecasting through conceptual models, part Ⅰ, a discussion of principles [J].J Hydrol, 1970, 27(3):282-290.

Memo

Memo:

Biographies: Martins Y.Otache(1968—), male, graduate, Nigerian, martinso3@yahoo.com;Li Zhijia(corresponding author), male, doctor, professor, lizhija@vip.sina.com.

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