Prediction of Longitudinal Dispersion Coefficient in Natural Streams using Soft Computing Techniques

Document Type : Research Paper

Authors

1 Assistant Professor, Faculty of Agriculture and Natural Resources, Ardakan University

2 PhD Candidate, Water Engineering, Isfahan University of Technology

Abstract

To accurately estimate the longitudinal dispersion coefficient is important and indispensable in river modeling. Many theoretical as well as empirical formulations have been proposed to determine the longitudinal dispersion coefficient, but these have not been put into consideration because of their great error, and as well the complexity of the phenomenon. The main aim followed in the present paper is to investigate the method as well as equations developed for dispersion coefficient estimation and assessment of the accuracy of these methods in comparison with real data and developing an accurate methodology for dispersion coefficient determination making use of such soft computing techniques as, neural, genetic programming and Neuron-Fuzzy Inference System.ANFIS approach ended up with the excellent results of: R2 = 0.87, RMSE = 72.21, CRM = 0.103 and EF=0.75 as compared with the existing predictors of dispersion coefficient. In total ANFIS approach is hereby proposed as a most acceptable technique for estimating the longitudinal dispersion coefficient.

Keywords

Main Subjects

References

Abedie-Kupaie, J., Nasri, Z., and Maamanpoosh, A. (2007). Invetigation of Chemical  quality of Zayandehrood river. 6 th Iran Hydraulic conference, Shahrekord University, 131-142. (In Farsi).
Adarsh, S. (2010). Prediction of Longitudinal Dispersion Coecient in Natural Channels Using Soft Computing Techniques, Transaction A: Civil Engineering. 17(5), 363-371.
Afzalimehr, H. (2011). Floud Mechanic Education, Esfahan, Arkan. (In Farsi).
Alvisi, S., Mascellani,G., Franchini, M., and Bardossy. A. (2005). Water level forecasting through fuzzy logic and artificial neural network approaches, Hydrology and Earth System Sciences Discussion, 2, 1107- 1145.
Amini, M., Abbaspour, K. C., Khademi, H., Fathianpour, N., Afyuni, M., and Schulin, R. (2005). Neural network models to predict cation exchange capacity in arid regions of Iran, European Journal of Soil Science. 53, 748–757.
Aytek, A. and Kisi. O. (2008). A genetic programming approach to suspended sediment modeling. Journal of Hydrology, 351, 288-298.
Azamathulla, H. M. and Ghani, A. A. (2010). Genetic Programming for Predicting Longitudinal Dispersion Coefficients in Streams. Water Resource Managment, 1-8.
Azamathulla, H. M. and Wu, F. C. (2011). Support vector machine approach for longitudinal dispersion coefficients in natural streams. Applied Soft Computing, 11(2), 2902-2905.
Borelli, A., De Falco, I., Della, C. A., Nicodemi, M., and Trautteur, G. (2006). Performance of genetic programming to extract the trend in noisy data series. Physica A. 370, 104-108.
Deng, Z. Q., Singh, V. P., and Bengtsson, L. (2001). Longitudinal dispersion coefficient in single channel streams. Journal of Hydraulic Engineering, 128(10), 901-916.
Firat, M. and Gungor, M. (2007). River flow estimation using adaptive neuro fuzzy inference system. Math & Comp in Simulation, 75, 87–96.
Fischer, H. B., List, E. J., Koh, R. C. Y., Imberger, J., and Brooks, N. H. (1979). Mixing in inland and costal waters, Academic Press, Inc., San Diego, 483.
Kashefipour, S. M. and Falconer, A. (2002). Longitudinal dispersion coefficients in Natural channels. Water Research, 36, 1596-1608.
Koza, J. R. (1992). Genetic Programming: on the programming of computers by means of natural selection. The MIT Press, Cambridge, MA.
Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference systems, IEEE Trans Systems Man Cybernet 23, 665- 685.
Li, Z. H., Huang, J., and Li, J. (1998). Preliminary study on longitudinal dispersion coefficient for the Gorges reservoir. Proc. of the 7th International Symposium Environmental Hydraulics, 16-18. December, Hong Kong, China.
McQuivey, R. S. and Keefer, T. N. (1974). Simple method for predicting dispersion in streams, Journal of Environmental Engineering, ASCE, 100(4): 997–1011.
Menhaj, M. (2009). Principle of Neuron network and Artificial Intelligence, Amir Kabir University. (In Farsi).
Minasny, B., McBratney, A. B., and Bristow, K. L. (1999). Comparison of different approaches to the development of pedotransfer functions for waterretention curves. Geoderma, 93, 225–253.
Purabadehie, M., Tokeldani, M., and Liyaghat, A. (2003). Invetigation of flow parameters effect on Dispersion Coefficient of Pollutants in rectangular Chanel. 6 th Iran Hydraulic conference, Shahrekord University, 29-38. (In Farsi).
Rajeev, R. S. and Dutta, S. (2009). Prediction of longitudinal dispersion coefficients in natural rivers using genetic algorithm. Journal of Hydrolic Research, 40(6):544–552.
Rajeev, R. S. (2013). Predicting longitudinal dispersion coefficients in sinuous rivers by genetic algorithm, Journal of Hydrology Hydromechanics, 61, 3, 214–221.
Riahi-Madvar, H. and Ayyoubzadeh, S. A. (2007). Estimating Longitudinal Dispersion Coefficient of Pollutants Using daptive Neuro-Fuzzy Inference System, Isfahan Journal of Water and Wastewater, 64, 15–27. (In Farsi).
Riahi-Madvar, H., Ayyoubzadeh, S. A., Khadangi, E., and Ebadzadeh, M. M. (2009). An expert system for predicting longitudinal dispersion coefficient in natural streams by using ANFIS. Expert System with Applications, 36(4): 8589–8596.
Seo, I. W. and Cheong, T. S. (1998). Predicting longitudinal dispersion coefficient in natural stream. Journal of Hydraulics Engineering, 124 (1), 25-32.
Shaban, K., El-Hag, A., and Matveev, A. (2009) A Cascade of Artificial Neural Networks to Predict Transformers Oil Parameters. IEEE Transactions on Dielectrics and Electrical Insulation. 16(2): 516-523.
Tavakollizadeh, A. and Kashefipur, S. M. (2007). Effects of dispersion coefficient on quality modeling of surface waters. In: Proceedings of the sixth international symposium river engineering, 16–18.October, Ahwaz, Iran, pp 67–78. (In Farsi).
Tayfur, G. (2009). Optimized model predicts longitudinal dispersion coefficient in natural channels, Hydrology Research, 40(1), 60-78.
Tayfur, G. and Singh, V. P.(2005). Predicting longitudinal dispersion coefficient in natural streams by artificial neural network. Journal of Hydraulic Engineering, 131 (11), 991-1000.
Toprak, Z. F. and Savci, M. E. (2007). Longitudinal Dispersion Coefficient Modeling in Natural Channels using Fuzzy Logic. Clean 35(6): 626–637.
Toprak, Z. F. and Cigizoglu, H. K. (2008). Predicting longitudinal dispersion coefficient in natural streams by artificial intelligence methods. Hydrology Process, 22: 4106–4129.
Toprak, Z. F., Hamidi, N., Kisi, O., and Gerger, R. (2013). Modeling Dimensionless Longitudinal Dispersion Coefficient in Natural Streams using Artificial Intelligence Methods. KSCE Journal of Civil Engineering . In press. DOI 10.1007/s12205-014-0089-y.
Iranian Journal of Soil and Water Research
Volume 46, Issue 3 - Serial Number 3
October 2015
Pages 385-394

Files

Share

How to cite

Statistics