Estimation of Daily Suspended Sediment Load by Using Wavelet Conjunction Models
Publication: Journal of Hydrologic Engineering
Volume 17, Issue 9
Abstract
Accurate estimation of sediment loads is important for the management and construction of water resources projects. In the first part of this study, the convenient gene expression programming (GEP), neuro-fuzzy (NF), and artificial neural network (ANN) techniques were applied to estimate suspended sediment loads by using recorded daily river discharge and sediment load data. These models were compared with one another in terms of the coefficient of determination, root mean square error, mean absolute error, variance accounted for, and Nash-Sutcliffe statistic criteria. It was found that the GEP model performed better than the NF and ANN models. In the second part of this study, the discrete wavelet conjunction models with convenient GEP, NF, and ANN techniques were constructed and compared with one another. Comparison results indicated that the wavelet conjunction models significantly increased the accuracy of single GEP, NF, and ANN models in suspended sediment estimation. The wavelet-GEP model performed better than the wavelet-NF and wavelet-ANN models.
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References
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000a). “Artificial neural networks in hydrology. I: Preliminary concepts.” J. Hydrol. Eng., 5(2), 115–123.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000b). “Artificial neural networks in hydrology. II: Hydrologic applications.” J. Hydrol. Eng., 5(2), 124–137.
Aytek, A., and Kisi, O. (2008). “A genetic programming approach to suspended sediment modeling.” J. Hydrol., 351(3–4), 288–298.
Bayazıt, M., and Oguz, B. (1998). Probability and statistics for engineers, Birsen Publishing House, Istanbul, Turkey.
Cigizoglu, H. K. (2002). “Suspended sediment estimation and forecasting using artificial neural networks.” Turkish J. Eng. Env. Sci., 26, 15–25.
Cigizoglu, H. K. (2004). “Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons.” Adv. Water Resour., 27(2), 185–195.
Cobaner, M., Unal, B., and Kisi, O. (2009). “Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data.” J. Hydrol., 367(1–2), 52–61.
Coulibaly, P., and Burn, H. D. (2004). “Wavelet analysis of variability in annual Canadian streamflows.” Water Resour. Res., 40(W03105–W0318), 105.
Cybenco, G. (1989). “Approximation by superposition of a sigmoidal function.” Math. Control Signals Syst., 2(4), 303–314.
Ferreira, C. (2001a). “Gene expression programming in problem solving.” 6th Online World Conf. on Soft computing in Industrial Applications (invited tutorial).
Ferreira, C. (2001b). “Gene expression programming: A new adaptive algorithm for solving problems.” Complex Syst., 13(2), 87–129.
Ferreira, C. (2006). Gene expression programming: Mathematical Modeling by an artificial intelligence, Springer, Berlin.
Fortin, J. G., Anctil, F., Parent, L. E., and Bolinder, M. A. (2008). “Comparison of empirical daily surface incoming solar radiation methods.” Agric. For. Meteorol., 148(8–9), 1332–1340.
Gaucherel, C. (2002). “Use of wavelet transform for temporal characterization of remote watersheds.” J. Hydrol., 269(3–4), 101–121.
Giustolisi, O. (2004). “Using GP to determine Chezzy resistance coefficient in corrugated channels.” J. Hydroinf., 6(3), 157–173.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning, Addison–Wesley, Reading MA.
Hagan, M. T., and Menhaj, M. B. (1994). “Training feed forward networks with the Marquaradt algorithm.” IEEE Trans. Neural Networks, 5(6), 861–867.
Haykin, S. (1998). Neural networks—A comprehensive foundation, 2nd Ed., Prentice-Hall, Upper Saddle River, NJ, 26–32.
Jain, S. K. (2001). “Development of integrated sediment rating curves using ANNs.” J. Hydrol. Eng., 127(1), 30–37.
Jang, J. S. R. (1993). “ANFIS: Adaptive-network-based fuzzy inference system.” IEEE Trans. Syst. Manag. Cyber., 23(3), 665–685.
Jang, J. S. R., and Sun, C. T. (1995). “Neuro fuzzy modeling and control.” Proc IEEE., 83(3), 378–405.
Jang, J. S. R., Sun, C. T., and Mizutani, E. (1997). Neurofuzzy and soft computing: A computational approach to learning and machine intelligence, Prentice-Hall, NJ.
Karim, M. F., and Kennedy, J. F. (1990). “Menu of coupled velocity and sediment—discharge relations for rivers.” J. Hydrol. Eng., 116(8), 978–996.
Kim, T. W., and Valdes, J. B. (2003). “Nonlinear model for drought forecasting based on a conjunction wavelet and neural networks.” J. Hydrol. Eng., 8(6), 319–328.
Kisi, O. (2004). “Multi-layer perceptions with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation.” Hydrol. Sci. J., 49(6), 1025–1040.
Kisi, O. (2005). “Suspended sediment estimation using neuro-fuzzy and neural network approaches.” Hydrol. Sci. J., 50(4), 683–696.
Kisi, O. (2009a). “Evolutionary fuzzy models for river suspended sediment concentration estimation.” J. Hydrol., 372(1–4), 68–79.
Kisi, O. (2009b). “Neural networks and wavelet conjunction model for intermittent stream flow forecasting.” J. Hydrol. Eng., 14(8), 773–782.
Kisi, O., and Shiri, J. (2010). “A comparison of genetic programming and ANFIS in forecasting daily, monthly and daily streamflows.” In: Proc. Int. symp. on Innovations in Intelligent Systems and Applications, Kayseri and Cappadocia, Turkey, 118–122.
Kisi, O., and Shiri, J. (2011). “Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models.” Water Resour. Manage., 25(13), 3135–3152.
Kisi, O., Shiri, J., and Makarynskyy, O. (2011). “Wind speed prediction by using different wavelet conjunction models.” Int. J. Ocean. Climat. Syst. (IJOCS), 2(3), 189–208.
Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection, The MIT Press, Cambridge, MA.
Kucuk, M., and Agiralioglu, N. (2006). “Wavelet regression techniques for stremflow predictions.” J. Appl. Stat., 33(9), 943–960.
Kumar, A. R. S., Ojha, C. S. P., Goyal, M. K., Singh, R. D., and Swamee, P. K. (2011). “Modelling of suspended sediment concentration at kasol in india using ann, fuzzy logic and decision tree algorithms.” J. Hydrol. Eng., 16(3), 394–404.
Labat, D. (2005). “Recent advances in wavelet analyses: Part 1. A review of concepts.” J. Hydrol., 314(1–4), 275–288.
Labat, D. (2008). “Wavelet analysis of the annual discharge records of the world’s largest rivers.” Adv. Water Resour., 31(1), 109–117.
Legates, D. R., and McCabe, G. J. (1999). “Evaluating the use of goodness-of-fit measures in hydrologic and hydroclimatic model validation.” Water Resour. Res., 35(1), 233–241.
Lopes, V. L., and Ffolliott, P. F. (1993). “Sediment rating curves for a clearcut Ponderosa Pine watershed in northern Arizona.” Water Resour. Bull., 29(3), 369–382.
Mallat, S. G. (1989). “A theory for multi resolution signal decomposition: The wavelet representation.” IEEE Trans. Pattern Anal. Mach. Intell., 11(7), 674–693.
Mamdani, E. H., and Assilian, S. (1975). “An experiment in linguistic synthesis with a fuzzy logic controller.” Inter. J. Man Mach. Stud., 7(1), 1–13.
McBean, E. A., and Al-Nassri, S. (1988). “Uncertainty in suspended sediment transport curves.” J. Hydraul. Eng., 114(1), 63–74.
Milne, A. E., Macleod, C. J. A., Haygarth, P. M., Hawkins, J. M. B., and Lark, R. M. (2009). “The wavelet packet transform: A technique for investigating temporal variation of river water solutes.” J.Hydrol., 379(1–2), 1–19.
Partal, T., and Küçük, M. (2006). “Long-term trend analysis using discrete wavelet components of annual precipitations measurements in Marmara region (Turkey).” Phys. Chem. Earth., 31(18), 1189–1200.
Partal, T., and Kisi, O. (2007). “Wavelet and neuro fuzzy conjunction model for precipitation forecasting.” J. Hydrol., 342(1–2), 199–212.
Partal, T., and Cigizoglu, H. K. (2008). “Estimation and forecasting of daily suspended sediment data using wavelet-neural networks.” J. Hydrol., 358(3–4), 317–331.
Sandy, R. (1990). Statistics for business and economics, McGraw-Hill Publishing, New York.
Shiri, J., and Kisi, O. (2010). “Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model.” J. Hydrol., 394(3–4), 486–493.
Shiri, J., and Kisi, O. (2011a). “Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations.” Comput. Geosci., 37(10), 1692–1701.
Shiri, J., and Kisi, O. (2011b). “Application of artificial intelligence to estimate daily pan evaporation using available and estimated climatic data in the Khozestan Province (South Western Iran).” J. Irrig. Drain. Eng., 137(7), 412–425.
Sivakumar, B. (2006). “Suspended sediment load estimation and the problem of inadequate data sampling: A fractal view.” Earth Surf. Processes Landforms, 31(4), 414–427.
Smith, L. C., Turcotte, D. L., and Isacks, B. (1998). “Stream flow characterization and feature detection using a discrete wavelet transform.” Hydrol. Processes, 12(2), 233–249.
Takagi, T., and Sugeno, M. (1985). “Fuzzy identification of systems and its application to modeling and control.” IEEE Trans. Syst. Man. Cybern., 15(1), 116–132.
Vanoni, V. A. (1971). “Sediment discharge formulas.” J. Hydraulic Div., 97(4), 523–567.
Vernieuwe, H., Georgieva, O., De Baets, B., Pauwels, V. R. N., Verhoest, N. E. C., and De Troch, F. P. (2005). “Comparison of data-driven Takagi-Sugeno models of rainfall-discharge dynamics.” J. Hydrol., 302(1–4), 173–186.
Wang, W., and Ding, J. (2003). “Wavelet network model and its application to the prediction of the hydrology.” Nat. Sci., 1(1), 67–71.
Wang, W., Cahu, K. W., Cheng, C. T., and Qiu, L. (2009a). “A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series.” J. Hydrol., 374(3–4), 294–306.
Wang, W., Jin, J., and Li, Y. (2009b). “Prediction of inflow at Three Gorges Dam in Yangtze River with wavelet network model.” Water Resour. Manage., 23(13), 2791–2803.
Xingang, D., Ping, W., and Jifan, C. (2003). “Multiscale characteristics of the rainy season rainfall and interdecadal decaying of summer monsoon in North China.” Chin. Sci. Bull., 48(24), 2730–2734.
Zhou, H. C., Peng, Y., and Liang, G. H. (2008). “The research of monthly discharge predictor-corrector model based on wavelet decomposition.” Water Resour. Manage., 22(2), 217–227.
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Published In
Journal of Hydrologic Engineering
Volume 17 • Issue 9 • September 2012
Pages: 986 - 1000
Copyright
© 2012 American Society of Civil Engineers.
History
Received: Jun 7, 2011
Accepted: Oct 26, 2011
Published online: Oct 29, 2011
Published in print: Sep 1, 2012
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