Forecasting of Mean Daily Runoff Discharge of Behesht-Abad River Using Wavelet Analysis

آب و خاک

Article 6, Volume 28, Issue 3, July 2014, Pages 534-545   

Document Type: Research Article

DOI: 10.22067/jsw.v0i0.29201

Authors

Sajjad Abdollahi Asadabadi^{* 1}; yaghoub dinpazhoh; Rasoul Mirabbasi²

¹Shahid Chamran University of Ahvaz

²Shahrekord University

Abstract

Forecasting of river discharge is a key aspect of efficient water resources planning and management. In this study, two models based on Wavelet Analysis and Artificial Neural networks (ANNs) were developed for forecasting discharge of Behesht-Abad River. For this purpose, mean daily discharge data of mentioned river as well as precipitation data of 17 meteorological stations were used in the period 1999-2008. In the first method, called Cross Wavelet (CW), complex Morlet wavelet was used as analyzer function. Wavelet analyzing was performed for every daily rainfall and average discharge time series, separately. Initial phase, phase differences of subseries obtained from wavelet analysis, and calibration coefficients were calculated. Then structural series were reconstructed and average of structural components calculated. The river discharges were predicted for 1, 2, 3 and 7 days ahead forecasting horizon. In the second method, called Wavelet Neural Networks conjunction (WNN), a preprocessing was done on the initial input matrix using Meyer wavelet. Then the elements of the initial input matrix were normalized and the second input matrix was created. A three layer Feed Forward Back Propagation (FFBP) was formed based on the second input matrix and target matrix. After training the model using Levenberg–Marquardt (LM) algorithm, the river discharges were predicted for short term time horizons. The results showed that the WNN method had higher accuracy in short-term forecasting of river discharge in comparison with CW and ANN methods.

Keywords

forecasting; River discharge; Cross Wavelet; Artificial neural network

References

بشری م.، و وفاخواه م. 1389. مقایسه روش های مختلف تحلیل سری های زمانی در پیش بینی دبی ماهانه حوزه آبخیز کرخه. فصلنامه علمی پژوهشی مهندسی آبیاری و آب، سال اول، شماره2، ص 86-75.

2- فتح آبادی ا.، سلاجقه ع.، و مهدوی م. 1387. پیش بینی دبی رودخانه با استفاده از روش های نوروفازی و مدل های سری های زمانی. مجله علمی پژوهشی علوم و مهندسی آبخیزداری ایران، سال دوم، شماره 5، ص 30-21.

3- کارآموز م.، و عراقی نژاد ش. 1384. هیدرولوژی پیشرفته. انتشارات دانشگاه صنعتی امیرکبیر.

4- منهاج م. 1380. مبانی شبکه های عصبی مصنوعی (جلد اول). انتشارات دانشگاه صنعتی امیرکبیر.

5- Adamowski J. 2008a. Development of a short-term river flood forecasting method for snowmelt driven and cross-wavelet analysis floods based on wavelet. Journal of Hydrology. 353: 247-266.

6- Adamowski J. 2008b. River flow forecasting using wavelet and cross-wavelet transform models. Journal of Hydrological Process. 22: 4877-4891.

7- Adamowski J., and Sun K. 2010. Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. Journal of Hydrology. 390: 85-91.

8- Chen B.H., Wan X.Z. Yang S.H., and McGreavy C. 1999. Application of wavelets and neural Networks to diagnostic System development, 1, feature extraction. Computers & Chemical Engineering, 23)7(: 899-906.

9- Coulibaly P., Anctil F., and Bobee B. 2000. Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. Journal of Hydrology. 230: 244–257.

10- Cannas B., Fanni A., See L., and Sias G. 2006. Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning. Phys. Chem. Earth. 31(18): 1164-1171.

11- Hagan M.T., and Menhaj M.B. 1994. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks. 5: 989-993.

12- Jury M.R., Enfield D.B., and Melice J.L. 2002. Tropical monsoons around Africa: stability of El Nino-southern oscillation associations and links with continental climate. Journal of Geophysical Research. 107 (C10): 3151 DOI: 10.1029/2000JC000507.

13- Kisi O. 2007. Streamflow forecasting using different Artificial Neural Network algorithms. Journal of Hydrologic Engineering. 12(5): 533-539.

14- Kisi O. 2008. Stream flow forecasting using neuro-wavelet technique. Journal of Hydrological Process. 22: 4142-4152.

15- Lettenmaier D.P., and Wood E.F. 1992. Hydrologic Forcasting. In: D.R Maidment (eds.). Handbook OF Hydrology. McGRAW-HILL, INC. 26.1-26.30.

16- Labat D. 2010. Cross wavelet analyses of annual continental freshwater discharge and selected climate indices. Journal of Hydrology. 385: 269-278.

17- Mallat S. 1989. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence11(7): 674-693.

18- Nourani V., Komasi M., and Mano A. 2009. A multivariate ANN-Wavelet approach for Rainfall-Runoff Modeling. Water Resources Management. 23: 2877-2894.

19- Polikar R. 1996. Fundamental Concepts and Over View of Wavelet Theory. Second Edition. Rowan University. College of Engineering Web Servers. Glassboro. NJ.08028.

20- Sarangi A. 2005. Comparison of Artificial Neural Network and regression models for sediment loss prediction from Banha watershed in India. Journal of Agriculture Water Management. 78: 195-208.

21- Torrence C., and Compo G.P. 1998. A practical guide to wavelet analysis. Bulletin of the American Meteorological Society. 79(1): 61-78.

22- Wang W. and Ding J. 2003. Wavelet network model and its application to the prediction of hydrology. Nature and Science. 1: 67-71.