Long-Lead Streamflow Forecasting using Artificial Neural Networks and Fuzzy Inference System

Document Type : Original Article

Authors

1 Ph. D., School of Civil and Environmental Engineering, Amirkabir University (Tehran Polytecchnic)

2 Professor, School of Engineering, University of Tehran

Abstract

Conceptual models have been often used in short- or mid-term hydrologic forecasting. In this paper a framework for combining two conceptual climatic and hydrologic models is used in order to generate long-lead Ensemble Streamflow Prediction (ESP) of streamflow to Zayandeh-rud reservoir. In the proposed approach, two models based on a Fuzzy Inference System (FIS) for seasonal rainfall forecasts and Artificial Neural Networks (ANNs) for mapping hydroclimatic variables to streamflow data are used. Illusions such as clustering of rainfall and streamflow data, a proper calibration procedure as well as using a stopped training approach in ANN calibration, improve the accuracy of the forecasts. The results of the proposed approach are assessed by various criteria. Further, the results are compared with an ANN-based streamflow forecast, which uses the observed hydroclimatic data in monthly streamflow forecasting. The results show that using the proposed approach has the advantage of generating proper long-lead point and ensemble forecasts, which could be potentially used to reflect the uncertainty of future available water resources. 

References

کارآموز، محمد (1382) "سیستم پشتیبانی در تصمیم‏گیری سد زاینده‏رود و مدیریت خشکسالی" دانشگاه صنعتی امیرکبیر-کارفرما: سازمان آب منطقه ای اصفهان
Anderson. M. L., M. L. Kavvas and Mierzwa, M. D. (2001), “Probabilistic/ensemble forecasting: a case study using hydrologic response distributions associated with El Niño/Southern Oscillation (ENSO)” Journal of Hydrology, 249, pp. 134-147.
Coulibaly, P., Anctil, F. and Bobée, B. (2000), “Daily reservoir inflow forecasting using artificial neural networks with stopped training approach”, Journal of Hydrology, 230, pp. 244-257.
Day, G. N. (1983), “Extended Streamflow Forecasting Using NWSRFS”, ASCE Water Resources Planning and Management, Vol. (111), No. 2. pp. 157-170.
Mamdani E. H. (1976), "Advances in linguistic synthesizes of fuzzy controllers”, J. Man mach. Stud., vol. 8, pp. 669-678.
Faber. B. A., and Stedinger. J. R., (2001), "Reservoir optimization using sampling SDP with ensemble streamflow prediction (ESP) forecast", Journal of Hydrology,. 249, pp. 113-133.
Hsu, K.-L., Gupta, H. V. and Sorooshian, S. (1995), “Artificial neural networks modeling of rainfall-runoff process”, Water Resources Research, 31(10), pp. 2517-2530.
Lettenmaier, D.P. and Wood. E.F. (1993), Hydrologic Forecasting, Handbook of Hydrology, Chap. 26, Maidment (Ed.) McGraw-Hill, Hightstown, NJ.
Pesti. G., Shresta.B. P., Duekstain.L., and Bogardi.I., (1996), "A fuzzy rule-based approach to drought assessment" Water Res. Res. Vol. 132. No. 6. pp. 1741-1747.
Piechota. T. C., Chiew. F. H. S., and Dracup. J. A. (1998), "Seasonal streamflow forecasting in eastern Australia and the El Nino – Southern Oscilation" Water Resou. Res. Vol. 34. No. 11, pp. 3035-3044.
Ross, T. J., (1995), Fuzzy Logic with Engineering Applications, McGraw-Hill, Hightstown, NJ, 600 pp.
Troup, A. J. (1965), “The southern oscillation.” Quarterly J. Royal Meteorological Soc., 91, pp. 490-506.
Iran-Water Resources Research
Volume 1, Issue 2
July 2005
Pages 29-41

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History

  • Receive Date: 25 November 2004
  • Accept Date: 13 April 2005

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