River Suspended Sediment Load Prediction: Application of ANN and Wavelet Conjunction Model
Publication: Journal of Hydrologic Engineering
Volume 16, Issue 8
Abstract
Accurate suspended sediment prediction is an integral component of sustainable water resources and environmental systems. This study considered artificial neural network (ANN), wavelet analysis and ANN combination (WANN), multilinear regression (MLR), and sediment rating curve (SRC) models for daily suspended sediment load () modeling in the Iowa River gauging station in the United States. In the proposed WANN model, discrete wavelet transform was linked to the ANN method. For this purpose, observed time series of river discharge () and were decomposed into several subtime series at different scales by discrete wavelet transform. Then these subtime series were imposed as inputs to the ANN method to predict one-day-ahead . The results showed that the WANN model was in good agreement with the observed values and that it performed better than the other models. The coefficient of efficiency was 0.81 for the WANN model and 0.67, 0.6, and 0.39 for the ANN, MLR, and SRC models, respectively. In addition, the WANN model presented relatively reasonable predictions for extreme values, acceptably simulated the hysteresis phenomenon, and satisfactorily estimated the cumulative suspended sediment load. Wavelet transforms provide useful decompositions of primary time series, so that wavelet-transformed data improve the ability of a predicting model by capturing useful information on various resolution levels. The proposed WANN model can be considered as a relatively new application of a combined wavelet and ANN model for suspended sediment prediction.
Get full access to this article
View all available purchase options and get full access to this article.
References
Addison, P. S., Murrary, K. B., and Watson, J. N. (2001). “Wavelet transform analysis of open channel wake flows.” J. Eng. Mech., 127(1), 58–70.
Alp, M., and Cigizoglu, H. K. (2007). “Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data.” Environ. Model. Software, 22(1), 2–13.
ASCE Task Committee on Application of ANNs in Hydrology. (2000). “Artificial neural networks in hydrology, II: Hydrologic application.” J. Hydrol. Eng., 5(2), 124–137.
Asselman, N. E. M. (2000). “Fitting and interpretation of sediment rating curves.” J. Hydrol. (Amsterdam), 234(3-4), 228–248.
Bhattacharya, B., Price, R. K., and Solomatine, D. P. (2005). “Data-driven modelling in the context of sediment transport.” Phys. Chem. Earth, 30(4-5), 297–302.
Cannas, B., Fanni, A., Sias, G., Tronei, S., and Zedda, M. K. (2005). “River flow forecasting using neural networks and wavelet analysis.” EGU 2005, European Geosciences Union, Vienna, Austria, 24–29.
Cannas, B., Fanni, A., See, L., and Sias, G. (2006). “Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning.” Phys. Chem. Earth , 31(18), 1164–1171.
Cigizoglu, H. K. (2004). “Estimation and forecasting of daily suspended sediment data by multi layer perceptrons.” Adv. Water Resour., 27(2), 185–195.
Cigizoglu, H. K., and Kisi, O. (2005). “Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data.” Nord. Hydrol., 36(1), 49–64.
Cigizoglu, H. K., and Kisi, O. (2006). “Methods to improve the neural network performance in suspended sediment estimation.” J. Hydrol., 317(3-4), 221–238.
Engelund, F., and Hansen, E. (1967). A monograph on sediment transport in alluvial streams, Danish Technical (Teknisk Forlag), Copenhagen, Denmark.
Fernando, A. K., and Kerr, T. (2003). “Runoff forecasting with artificial neural network model.” 3rd Pacific Conf. on Stormwater and Aquatic Resource Protection, New Zealand Water and Wastes Association, Auckland, New Zealand.
Foufoula-Georgiou, E., and Kumar, P. (1995). Wavelet in geophysics, Academic Press, New York, 337.
Gallant, S. I. (1993). Neural network learning and expert systems, MIT Press, Cambridge, UK.
Grossman, A., and Morlet, J. (1984). “Decomposition of hardy functions into square sintegrable wavelets constant shapes.” J. Math. Anal. Appl., 15, 723–736.
Haykin, S. (1999). Neural network: A comprehensive foundation, Prentice Hall, Englewood Cliffs, NJ.
Haykin, S. (2002). Adaptive filter theory, Prentice Hall, Englewood Cliffs, NJ.
He, L., Huang, G. H., Zeng, G. M., and Lu, H. W. (2008). “Wavelet-based multiresolution analysis for data cleaning and its application to water quality management systems.” Expert Syst. Appl., 35(3), 1301–1310.
Hornik, K., Stinchcombe, M., and White, H. (1989). “Multilayer feedforward networks are universal approximators.” Neural Netw., 2(5), 359–366.
Iadanza, C., and Napolitano, F. (2006). “Sediment transport time series in the Tiber River.” Phys. Chem. Earth , 31(18), 1212–1227.
Jain, S. K. (2008). “Development of integrated discharge and sediment rating relation using a compound neural network.” J. Hydrol. Eng., 13(3), 124–131.
Jayawardena, A. W., Xu, P. C., Tsang, F. L., and Li, W. K. (2006). “Determining the structure of a radial basis function network for prediction of nonlinear hydrological time series.” Hydrol. Sci. J., 51(1), 21–44.
Kim, T. W., and Valdes, J. B. (2003). “Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks.” J. Hydrol. Eng., 8(6), 319–328.
Kisi, O. (2004a). “River flow modeling using artificial neural networks.” J. Hydrol. Eng., 9(1), 60–63.
Kisi, O. (2004b). “Multi-layer perceptions with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation.” Hydrol. Sci. J., 49(6), 1.
Kisi, O. (2005). “Suspended sediment estimation using neuro-fuzzy and neural network approaches.” Hydrol. Sci. J., 50(4), 683–696.
Kisi, O. (2007). “Streamflow forecasting using different artificial neural network algorithms.” J. Hydrol. Eng., 12(5), 532–539.
Kisi, O. (2008). “Stream flow forecasting using neuro-wavelet technique.” Hydrol. Processes, 22(20), 4142–4152.
Labat, D. (2005). “Recent advances in wavelet analyses, part 1: A review of concepts.” J. Hydrol. (Amsterdam), 314(1-4), 275–288.
Legates, D. R., and McCabe, G. J. Jr. (1999). “Evaluating the use of goodness-of-fit measures in hydrologic and hydroclimatic model validation.” Water Resour. Res., 35(1), 233–241.
Liu, H. L., Chen, X., Bao, A. M., and Wang, L. (2007). “Investigation of groundwater response to overland flow and topography using a coupled MIKE SHE/MIKE 11 modeling system for an arid watershed.” J. Hydrol. (Amsterdam), 347(3-4), 448–459.
Mallat, S. G. (1989). “A theory for multiresolution signal decomposition: The wavelet representation.” IEEE Trans. Pattern Anal. Mach. Intell., 11(7), 674–693.
Mallat, S. G. (1998). A wavelet tour of signal processing, Academic Press, San Diego, 557.
Masters, T. (1993). Practical neural network recipes in C++, Academic Press, San Diego.
McCulloch, W. S., and Pitts, W. H. (1943). “A logical calculus of the ideas immanent in neural nets.” Bull. Math. Biophys., 5(4), 115–133.
Monsef, H., and Lotfifard, S. (2007). “Internal fault current identification based on wavelet transform in power transformers.” Electr. Power Syst. Res., 77(12), 1637–1645.
Morgan, R. P. C. (1995). Soil erosion and conservation, 2nd Ed., Longman, London.
Nadal-Romero, E., Regüés, D., and Latron, J. (2008). “Relationships among rainfall, runoff, and suspended sediment in a small catchment with badlands.” Catena, 74(2), 127–136.
Nagy, H. M., Watanabe, K., and Hirano, M. (2002). “Prediction of load concentration in rivers using artificial neural network model.” J. Hydraul. Eng., 128(6), 588–595.
Nash, J. E., and Sutcliffe, J. V. (1970). “River flow forecasting through conceptual models, part I: A discussion of principles.” J. Hydrol. (Amsterdam), 10(3), 282–290.
Nordahl, K., Martinius, A. W., and Kritski, A. (2006). “Time-series analysis of a heterolithic, ripple-laminated deposit (Early Jurassic, Tilje Formation) and implications for reservoir modeling.” Mar. Geol., 235(1-4), 255–271.
Nourani, V., Alami, M. T., and Aminfar, M. H. (2009a). “A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation.” Eng. Appl. Artif. Intell., 22(3), 466–472.
Nourani, V., Komasi, M., and Mano, A. (2009b). “A multivariate ANN-wavelet approach for rainfall-runoff modeling.” Water Resour. Manage., 23(14), 2877–2894.
Nourani, V., Mogaddam, A. A., and Nadiri, A. O. (2008). “An ANN-based model for spatiotemporal groundwater level forecasting.” Hydrol. Processes, 22(26), 5054–5066.
Onkal-Engin, G., Demir, I., and Engin, S. N. (2005). “Determination of the relationship between sewage odour and BOD by neural networks.” Environ. Model. Software, 20(7), 843–850.
Osowski, S., and Garanty, K. (2007). “Forecasting of the daily meteorological pollution using wavelets and support vector machine.” Eng. Appl. Artif. Intell., 20(6), 745–755.
Partal, T., and Cigizoglu, H. K. (2008). “Estimation and forecasting of the daily suspended sediment data using wavelet-neural networks.” J. Hydrol. (Amsterdam), 358(3-4), 317–331.
Pasquini, A., and Depetris, P. (2007). “Discharge trends and flow dynamics of South American rivers draining the southern Atlantic seaboard: An overview.” J. Hydrol. (Amsterdam), 333(2-4), 385–399.
Prechelt, L. (1998). “Early stopping-but when?” Lect. Notes Comput. Sci., 1524, 55–69.
Raghuwanshi, N., Singh, R., and Reddy, L. (2006). “Runoff and sediment yield modeling using artificial neural networks: Upper Siwane River, India.” J. Hydrol. Eng., 11(1), 71–79.
Rajaee, T. (2010). “Wavelet and neuro-fuzzy conjunction approach for suspended sediment prediction.” Clean: Soil, Air, Water, 38(3), 275–286.
Rajaee, T., Mirbagheri, S. A., Zounemat-Kermani, M., and Nourani, V. (2009). “Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models.” Sci. Total Environ., 407(17), 4916–4927.
Rovira, A., and Batalla, R. J. (2006). “Temporal distribution of suspended sediment transport in a Mediterranean basin: The Lower Tordera (NE Spain).” Geomorphology, 79(1-2), 58–71.
Sarma, J. N. (1986). “Sediment transport in the Burhi Dihing River, India.” Drainage basin sediment delivery, R. F. Hadley, ed., IAHS Press, Oxfordshire, UK, 159, 199–215.
Schmitz, J. E., Zemp, R. J., and Mendes, M. J. (2006). “Artificial neural networks for the solution of the phase stability problem.” Fluid Phase Equilib., 245(1), 83–87.
Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2002). “Artificial neural network-based settlement prediction formula for shallow foundations on granular soils.” Australian geomechanics Journal, 37(4), 45–52.
Smith, M. (1994). Neural networks for statistical modeling, Van Nostrand Reinhold, New York.
Snedecor, G. W., and Cochran, W. G. (1981). Statistical methods, 7th Ed., Iowa State Univ. Press, Ames, IA.
Szilagyi, J., Katul, G. G., Parlange, M. B., Albertson, J. D., and Cahill, A. T. (1996). “The local effect of intermittency on the inertial subrange energy spectrum of the atmospheric surface layer.” Bound.-Lay. Meteorol., 79(1-2), 35–50.
Tantanee, S., Patamatammakul, S., Oki, T., Sriboonlue, V., and Prempree, T. (2005). “Coupled wavelet-autoregressive model for annual rainfall prediction.” J. Environ. Hydrol., 13, 1–8.
Toffaleti, F. B. (1969). “Definitive computations of sand discharge in rivers.” J. Hydraul. Div., 95(1), 1831–1842.
Tokar, A. S., and Johnson, P. A. (1999). “Rainfall runoff modelling using artificial neural networks.” J. Hydrol. Eng., 4(3), 232–239.
Van Rijn, L. C. (1993). “Principles of sediment transport in rivers.” Estuaries and coastal areas, Aqua Publications, Amsterdam, Netherlands.
Verstraeten, G., and Poesen, J. (2001). “Factors controlling sediment yield from small intensively cultivated catchments in a temperate humid climate.” Geomorphology, 40(1-2), 123–144.
Walling, D. E. (1977). “Assessing the accuracy of suspended sediment rating curves for a small basin.” Water Resour. Res., 13(3), 531–538.
Yang, C.-S., and Nio, S.-W. (1985). “The estimation of palaehydrodynamic processes from subtidal deposits using time series analysis methods.” Sedimentology, 32(1), 41–57.
Yang, C. T. (1996). Sediment transport, theory and practice, McGraw-Hill, New York.
Yang, G., Chen, Z., Yu, F., Wang, Z., Zhao, Y., and Wang, Z. (2007). “Sediment rating parameters and their implications: Yangtze River, China.” Geomorphology, 85(3-4), 166–175.
Youssef, O. A. S. (2003). “A wavelet-based technique for discrimination between faults and magnetizing inrush currents in transformers.” IEEE Trans. Power Delivery, 18(1), 170–176.
Zhu, Y. M., Lu, X. X., and Zhou, Y. (2007). “Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjiang River in the Upper Yangtze Catchment, China.” Geomorphology, 84(1-2), 111–125.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 16 • Issue 8 • August 2011
Pages: 613 - 627
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Sep 16, 2009
Accepted: Oct 24, 2010
Published online: Oct 29, 2010
Published in print: Aug 1, 2011
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.