Document Type : Research Article
Authors
Shahid Chamran University of Ahvaz
Abstract
Today, the daily flow forecasting of rivers is an important issue in hydrology and water resources and thus can be used the results of daily river flow modeling in water resources management, droughts and floods monitoring. In this study, due to the importance of this issue, using nonlinear time series models and artificial intelligence (Artificial Neural Network and Gen Expression Programming), the daily flow modeling has been at the time interval (1981-2012) in the Armand hydrometric station on the Karun River. Armand station upstream basin is one of the most basins in the North Karun basin and includes four sub basins (Vanak, Middle Karun, Beheshtabad and Kohrang).The results of this study shown that artificial intelligence models have superior than nonlinear time series in flow daily simulation in the Karun River. As well as, modeling and comparison of artificial intelligence models showed that the Gen Expression Programming have evaluation criteria better than artificial neural network.
Keywords
1- Ainkaran P. 2004. Analysis of some linear and nonlinear time series models. A thesis submitted in fulfillment of the requirements for the degree of Master of Science, School of Mathematics and Statistics, University of Sydney.
2- Akaike H. 1974. A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control, AC-19, 716–723.
3- Alvisi S., Mascellani G., Franchini M., and Bardossy A. 2005. Water level forecasting through fuzzy logic and artificial neural network approaches. J Hydrol Earth Sys, Sci. 2: 1107-1145.
4- Aytek A., and Asce M. 2008. An application of artificial intelligence for rainfall runoff modeling. J Hydrol Earth Syst, Sci. 117(2): 145-155.
5- Baareh A.K.M., Sheta A.F., and Khnaifes K.A. 2006. Forecasting river flow in the USA : A comparison between auto- regression and neural network non-parametric models. J Comput Sci, 2(10): 775-780.
6- Caiado J. 2007. Forecasting water consumption in Spain using univariate time series models. Munich Personal RePEc Archive, No: 6610.
7- Dai Y. and Billard L. 2003. Maximum likelihood estimation in space time bilinea models. Journal of Time Series Analysis, 24(1): 25-44.
8- Dannademehr A. and Tabatabai M.R.M. 2010. Prediction of Daily discharge trend of river flow based on genetic programming. J. of Water and soil. 24(2): 325-333. (In Persian).
9- Dogan E., Isik S., Toluk T., and Sandalci M. 2007. Daily streamflow forecasting using artificial neural networks. Pp. 448-459. International Congress River Flood Management. Ankara, Turkey.
10- Fan J., and Yao Q. 2003. Nonlinear time series, nonparametric and parametric methods. Springer-Verlag, NewYork, Inc.
11- Farbodfam N., Ghorbani M.A., and Aalami M.T. 2009. Forecasting river flow using genetic programming (Case study: Lighwan watershed). J. Agric. Sci. 19(1): 4. 107-123. (In Persian).
12- Ferreira C. 2001. Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems, 13(2): 87-129.
13- Firat M. 2007. Artificial intelligence techniques for river flow forecasting in the Seyhan river catchment, Turkey. J Hydrol Earth Syst, Sci. 4: 1369-1406.
14- Ghorbani M.A., Shiri J., and Kazemi H. 2010. Estimation of Maximum, Mean and Minimum Air Temperature in Tabriz City Using Artificial Intelligent Methods. J. Agric, Sci. 20(3): 4. 87-104. (In Persian).
15- Ghorbani M.A., Kisi O., and Aalinezhad M. 2010. A probe into the chaotic nature of daily streamflow time series by correlation dimension and largest Lyapunov methods. Applied Mathematical Modelling, 34: 4050–4057.
16- Govindaraju R.S. 2000. Artificial neural network in hydrology. Journal of hydrologic Engineering, 5(2): 115-123.
17- Granger C.W.J. and Andersen A.P. 1978. An Introduction to Bilinear Time Series Models. Vandenhoek and Ruprecht: Gottingen.
18- Guven A. 2009. Linear genetic programming for time-series modeling of daily flow rate. J Earth Syst, Sci. 118(2): 157-173.
19- Khalili K., Fakheri Fard A., Dinpashoh Y., and Ghorbani M.A. 2011. Nonlinearity and testing for stream flow processes by BDS test (Case study: Shaharchai river). J. Agric. Sci. 22(2): 25-37. (In Persian).
20- Khalili K., Ahmadi F., Abghari H., and Besharat S. 2013. Stationary and nonlinearity analysis of river flow at different time scales (Case study: Barandouz-Chai River). 9th International River Engineering Conference, Ahwaz, Iran.
21- Khu S.T., Liong S.Y., Babovic V., Madsen H., and Muttil N. 2001. Genetic programming and its application in real- time runoff forming. Journal of American Water Resources Association, 37(2): 439-451.
22- Kisi O. 2007. Streamflow Forecasting Using Different Artificial Neural Network Algorithms. Journal of Hydrologic Engineering, 12(5): 533-539.
23- Lifshits M.A. 2006. Invariance principle in a bilinear model with weak nonlinearity. Journal of Mathematical Sciences, 137(1), 4541-4545.
24- Lohani A.K., Kumar R. and Singh R.D. 2012. Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. J. Hydrology, 442–443, 23–35.
25- Naveh H., Khalili K., Alami M.T., and Behmanesh J. 2012. Forecasting River flow By Bilinear Nonlinear Time Series Model (Case Study: Barandoz-Chay & Shahar-Chai Rivers). J. of Water and soil, 26(5): 1299-1307. (In Persian).
26- Salas J.D. 1993. Analysis and Modeling of Hydrological Time Series. In: Handbook of Hydrology, edited by David R, Maidment, McGraw-Hill, New York, 19(72).
27- Subba Rao T., and Gabr M. 1984. An Introduction to Bispectral Analysis and Bilinear Time Series Models. Lecture Notes in Statistics, 24, Springer-Verlag: New York.
28- Tsay R.S. 2002. Analysis of financial time series. University of Chicago, A Wiley – Interscience Publication, John Wiley f Sons; Inc.
29- Tsonis A.A. 2001. Probing the linearity and nonlinearity in the transitions of the atmospheric circulation. Nonlinear Processes Geophysics, 8: 341-345.
30- Wang W., Van G., Pieter H.A.J.M., Vrijling J.K., and Ma J. 2005. Testing and modelling autoregressive conditional heteroskedasticity of stream flow processes. Nonlin. Processes Geophys., 12: 55–66, doi:10.5194/npg-12-55.
31- Wang W., Vrijling J.K., Pieter H.A.J.M., Van G., and Ma J. 2006. Testing for nonlinearity stream flow processes at different timescales. J. Hydrology, 322(1): 247-268.
)
Send comment about this article