Forecasting Low and High Monthly Discharge Using a Stochastic Model and Artificial Intelligence

Document Type : Original Article

Authors

1 Associate Professor, Departement of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources., Gorgan., Iran

2 Former MSc. student of Water Resources Engineering, faculty of Soil and Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources., Gorgan., Iran

Abstract

   Forecasting the low and high flow of rivers is really important, especially in areas where surface water resources are considered as the main source of drinking and agricultural purposes. In this paper, the monthly low and high flow time series  of Gorganrud river have been modeled and forecasted using a stochastic model(ARIMA) and Artificial Intelligence (ANN). For monthly Low and high flow, the moving average value of one, three, five and seven daily flow was considered. At the end, the low flow series had better result in Stochastic model and neural network. According to the mean absolute error (MAE), in 1 and 7 daily low flow series, Stochastic model, SARIMA, had better results, and in 3 and 5 daily low flow series, the artificial intelegence, ANN, show better results. In high flow series, in 1 daily time series, Stochastic model(SARIMA), and in 3, 5  and 7 daily time series, ANN were appropriate.

Keywords

References

نیرومند،ح.ع.1376، تحلیل سری­های زمانی، انتشارات دانشگاه فردوسی مشهد، ویرایش دوم، 404صفحه.
شریفان، ح. و ب. قهرمان، 1385. بررسی و ارزیابی پیش بینی باران با تکنیک سر­ی­های زمانی در استان گلستان. دومین کنفرانس مدیریت منابع آب ایران،اصفهان، بهمن ماه 1385.
Baareh,M.A.K., Sheta,A.F and Al Khnaifes,K.H.  2006. Forecasting river flow in the USA: A comparison between auto-regression and neural network non-parametric models. Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization, (SMO’06), Stevens Point, Wisconsin, USA, pp:7-12.
Box G.E.P., Jenkins,G.M. 1976, Time Series Analysis: Forecasting and Control. Holden Day Inc. San Francisco, CA.,2 edition,575p.
Box GEP, Jenkins,G.M. 1976, Time Series Analysis, Forecasting and Control. Holden Day Inc. San Francisco, CA.
Bhattacharya,B., Solomatine,D.P. 2000, Application of artificial neural network in stage-discharge relationship,4-th International Conference on Hydroinformatics, Iowa City, USA.
Cigizoglu,H.K. 2003,a. Incorporation of ARMA models into flow forecasting by artificial neural networks. Journal of  Environmetrics, 14. 417–427 .
Cigizoglu,H.K. 2003,b. Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydrological Sciences–Journal–des Sciences Hydrologiques, 48:3.349-361.
Danh,T.N., Phien,H.N., Gupta,A.D. 1999. Neural network models for river flow forecasting. http:..www.wrc.org.za.
Dibike,Y.B., Solomatine,D.P. 2001. River flow forecasting using artificial neural networks, Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere. 26:1. 1-7.
Firat,M. 2008. Comparison of Artificial Intelligence Techniques for river flow Forecasting. Hydrology and Earth Syst. Sci., 12:123–139.
 Frausto-Solis,J., Pita,E., Lagunas,J. 2008. Short-term Streamflow Forecasting: Arima vs Neural network, American conference on applied mathenatics(MATH '08), Harvard, Massachusetts, USA.
Keskin,M.E., Taylan,E.D. 2010. Artificial Intelligent Models For Flow Prediction: A Case Study On Alara Stream, Journal of Engineering Science and Design 1;1. 8-13.
Kisi,O. 2004. River Flow Modeling Using Artificial Neural Networks.  Journal of hydrologic engineering © ASCE. 60-63.
Kisi,O. 2007. Development of Streamflow-Suspended Sediment Rating Curve Using a Range Dependent Neural Network, International Journal of Science and Technology. 2:1.49-61.
Mustafa,M.R., Isa,M.H., Rezaur,R.B. 2011. A Comparison of Artificial Neural Networks for Prediction of Suspended Sediment Discharge in River-A Case Study in Malaysia. Journal of  World Academy of Science, Engineering and Technology . 81. 372-376.
Sharifan,H., 2006, Optimization of Cropping Pattern and Intensification Under Uncertain Quantity and Quality of Irrigation Water, Ph.d thesis,Tarbiat Modarres University,212p.
Sharifan,H and Ghahraman,B. 2007. Evaluation of rainfall forecasting in Golestan province using time series, Journal of Agricultural Sciences and Natural Resources, vol.14:3.196-209.
Zhou,H.,  Peng,Y., Liang,G. 2008. The Research of Monthly Discharge Predictor-corrector Model Based on Wavelet Decomposition. Earth and Environmental Science. Journal of Water Resources Management.  22:2. 217-227.
 
Iranian Journal of Irrigation & Drainage
Volume 8, Issue 3 - Serial Number 47
November and December 2014
Pages 510-518

Files

Share

How to cite

Statistics