The Stream Flow Prediction Model Using Fuzzy Inference System and Particle Swarm Optimization

Document Type : Research Paper

Authors

1 Assist. Prof. of Civil Eng., Dept. of Eng., Shahrekord University, Shahrekord

2 Assist. Prof. of Irrigation and Drainage, Pardis Abu Rayhan, Tehran University

Abstract

The aim of this study is the spatial prediction runoff using hydrometric and meteorological stations data. The research shows that usually there is a certain communication between the meteorological and hydrometric data of upstream basin and runoff rates in output basin. So, if can be extracted the rules related to historical data that recorded at stations, can be easily predicted runoff amount based on data measured. Accordingly, among the tools available, the fuzzy theory (with flexibility in developing fuzzy rules) can be provide the knowledge lies in the observed data to parameters prediction in real time. So, in this research the fuzzy inference system has been used for estimating runoff rates at stations located in the Taleghan river downstream using rain gage stations and hydrometric stations upstream. Because the inappropriate values associated with membership functions, the fuzzy system model can not provide correct value for the prediction. In this study, a combination of intelligence-based optimization algorithm and fuzzy theory developed to accelerate and improve modeling. The result of proposed model, optimum values to each membership function that related to dependent and independent variable extracted and based on it’s the runoff rates in rivers downstream predicted. The results of this study were shown that the high accuracy of proposed model compared with fuzzy inference system. Also based on proposed model can be more accurately the rate of runoff estimated for future conditions.

Keywords

References

1- Korepazan, A. (2005). Fuzzy sets theory foundation and application in water engineering problem modeling, Amirkabir Pub., Tehran. (In Persian)
2- Shaebanynia, F., and Saeidnia, F. (2006). Fuzzy theory using MATLAB, Khaniran Pub., Tehran. (In Persian)
3- Zahidi, R. (2001). The application of nero-fuzzy in industry, Eziran Publisher, Tehran. (In Persian)
4- Zadeh, L.A. (1965). “Fuzzy sets.” J. of Information and Control, 8(3), 338-353.
5- Bárdossy, A., Bronstert, A., and Merz, B. (1995). “1-, 2- and 3-dimensional modeling of water movement in the unsaturated soil matrix using a fuzzy approach.” J. of Advances in Water Resources, 18(4), 237-251.
6- Bárdossy, A., and Duckstein, L. (1995). Fuzzy rule-based modeling with applications to geophysical, biological and engineering systems, CRC Press Inc., Boca Raton, Florida, USA.
7- Fontane, D.G., Timothy, K.G., and Moncado, E. (1997). “Planning reservoir operations with imprecise objectives.” J. of Water Resources Planning and Management, 123(3), 154-162.
8- ABEBE, A.J., Solomatine, D.P., and Venneker, R.G.W. (2000). “Application of adaptive fuzzy rule-based models for reconstruction of missing precipitation events.” J. of Hydrological Sciences, 45(3), 425-436.
9- Xiong, L., Shamseldin, A.V., and O'Connor, K.M. (2001). “A nonlinear combination of the forecasts of rainfall-runoff models by the first-order takagi-sugeno fuzzy systems.” J. of Hydrology, 245(1), 196-217.
10- Coppola, E.A., Duckstein, L., and Davis, D. (2002). “Fuzzy rule-based methodology for estimating monthly groundwater recharge in a temperate watershed.” j. of hydrologic Engineering, 7(4), 326-335.
11- Luchetta, A., and Manetti, S. (2003). “A real time hydrological forecasting system using a fuzzy clustering approach.” J. of Computers and Geosciences, 29(9), 1111-1117.
12- Singh, S.R. (2007). “A simple method of forecasting based on fuzzy time series.” J. of Applied Mathematics and Computation, 186(1), 330-339.
13- Wu, C.L., and Chau, K.W. (2006). “Evaluation of several algorithms in forecasting flood.” Lecture Notes in Artificial Intelligence, 4031, 111-116.
14- Wu, C.L., and Chau, K.W. (2006). “A flood forecasting neural network model with genetic algorithm.” Int. J. of Environment and Pollution, 28(3/4), 261-273.
15- Tareghian, R., and Kashefipour, S.M. (2007). “Application of fuzzy systems and artificial neural networks for flood forecasting.” J. of Applied Sciences, 7(22), 3451-3459.
16- Kuok, K.K., Harun, S., and Shamsuddin, S.M. (2010). “Particle swarm optimization feedforward neural network for modeling runoff.” Int. J. of Environ. Sci. Tech., 7(1), 67-78.
17- Chau, K.W. (2006). “Particle swarm optimization training algorithm for ANNs in stage prediction of shing Mun river.” J. of Hydrology, 329(3-4), 363-367.
18- Rajabpur, R., and Afshar, M.H. (2008). “Optimized operation of serial pump stations using the PSO algorithm.” J. of Water and Environment, 66(2), 56-66.
Journal of Water and Wastewater; Ab va Fazilab ( in persian )
Volume 24, Issue 1 - Serial Number 1
Serial number:85
March 2013
Pages 112-124

Files

Share

How to cite

Statistics