Development of Integrated Discharge and Sediment Rating Relation Using a Compound Neural Network
Publication: Journal of Hydrologic Engineering
Volume 13, Issue 3
Abstract
The assessment of sediment transport in rivers is of vital importance in design and management of hydraulic structures such as dams, diversions, hydro-power projects, river training works, bridges, etc. Previously reported studies have shown that data driven techniques such as the artificial neural network (ANN) can give better results in modeling stage-discharge relations than the conventional rating curves. In view of the complexities of rating relationships, compound rating curves are frequently used in place of a single rating curve. Accordingly, this paper investigates the abilities of compound neural networks (CNNs) to model integrated stage-discharge-suspended sediment rating relationship. Using the data of two stations on the Mississippi River and one station on Conococheague Creek, CNNs were trained. A comparison of the results of applying a single ANN and a CNN shows that the estimates of CNN are closer to the observed values than those of single ANN.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The writer would like to thank the anonymous reviewers of this paper for their useful comments and suggestions which helped in improving the paper.
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: Hydrological applications.” J. Hydrol. Eng., 5(2), 124–137.
Campolo, M., Soldati, A., and Andreussi, P. (2003). “Artificial neural network approach to flood forecasting in the River Arno.” Hydrol. Sci. J., 48(3), 381–398.
Chang, F. J., and Chen, Y. C. (2001). “A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction.” J. Hydrol., 245, 153–164.
Cigizoglu, H. K. (2003). “Estimation, forecasting and extrapolation of river flows by artificial neural networks.” Hydrol. Sci. J., 48(3), 349–361.
Cigizoglu, H. K. (2004). “Estimation and forecasting of daily suspended sediment data by multi layer perceptrons.” Adv. Water Resour., 27, 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.
Clair, T. A., and Ehrman, J. M. (1998). “Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers.” Water Resour. Res., 34(3), 447–455.
Coulibaly, P., Anctil, F., and Bobee, B. (2001). “Multivariate reservoir inflow forecasting using temporal neural networks.” J. Hydrol. Eng., 6(5), 367–376.
Crawford, C. G. (1991). “Estimation of suspended sediment rating curves and mean suspended sediment loads.” J. Hydrol., 129, 331–348.
Deka, P., and Chadramouli, V. (2003). “A fuzzy neural network model for deriving the river stage-discharge relationship.” Hydrol. Sci. J., 48(2), 197–209.
Graf, W. H. (1971). Hydraulics of sediment transport, McGraw-Hill, New York.
Hu, T. S., Lam, K. C., and Ng, S. T. (2005). “A modified neural network for improving river flow prediction.” Hydrol. Sci. J., 50(2), 299–318.
Imrie, C. E., Durucan, S., and Korre, A. (2000). “River flow prediction using artificial neural networks: generalization beyond the calibration range.” J. Hydrol., 233, 138–153.
Jain, S. K. (2001). “Development of integrated sediment rating curves using ANNs.” J. Hydraul. Eng., 127(1), 30–37.
Jain, S. K., and Chalisgaonkar, D. (2000). “Setting up stage discharge relations using ANN.” J. Hydrol. Eng., 5(4), 428–433.
Jain, S. K., Das, D., and Srivastava, D. K. (1999). “Application of ANN for reservoir inflow prediction and operation.” J. Water Resour. Plann. Manage., 125(5), 263–271.
Jain, S. K., Singh, V. P., and van Genuchten, M. Th. (2004). “Analysis of soil water retention data using artificial neural networks.” J. Hydrol. Eng., 9(5), 415–420.
Kisi, O. (2004). “Multilayer perceptrons with Levenberg–Marquardt optimization algorithm for suspended sediment concentration prediction and estimation.” Hydrol. Sci. J., 49(6), 1025–1040.
Kisi, O. (2005). “Suspended sediment estimation using neurofuzzy and neural network approaches.” Hydrol. Sci. J., 50(4), 683–696.
Lekkas, D. F., Imrie, C. E., and Lees, M. J. (2001). “Improved nonlinear transfer function and neural network methods of flow routing for real-time forecasting.” J. Hydroinform., 03.3, 153–164.
Maier, H. R., and Dandy, G. C. (2000). “Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications.” Environmental modelling & software, Vol. 15, Elsevier, New York, 101–124.
Nayak, P. C., Sudheer, K. P., Rangan, D. M., and Ramasastri, K. S. (2004). “A neurofuzzy computing technique for modeling hydrological time series.” J. Hydrol., 291(1–2), 52–66.
Smith, J., and Eli, R. N. (1995). “Neural network models of rainfall-runoff process.” J. Water Resour. Plann. Manage., 121(6), 499–508.
Solomatine, D. P., and Dulal, K. N. (2003). “Model trees as an alternative to neural networks in rainfall-runoff modelling.” Hydrol. Sci. J., 48(3), 399–411.
Sudheer, K. P., and Jain, S. K. (2003). “Radial basis function neural network for modeling rating curves.” J. Hydrol. Eng., 8(3), 161–164.
Sudheer, K. P., Nayak, P. C., and Ramasastri, K. S. (2003). “Improving peak flow estimates in artificial neural network river flow models.” Hydrolog. Process., 17, 677–686.
Tayfur, G. (2002). “Artificial neural networks for sheet sediment transport.” Hydrol. Sci. J., 47(6), 879–892.
Tokar, A. S., and Johnson, P. A. (1999). “Rainfall-runoff modeling using artificial neural networks.” J. Hydrol. Eng., 4(3), 232–239.
Walling, D. E. (1977). “Assessing the accuracy of suspended sediment rating curves for a small basin.” Water Resour. Res., 13(3), 531–538.
Walling, D. E., and Webb, B. W. (1988). “The reliability of rating curve estimates of suspended sediment yield: Some further comments.” Sediment Budgets, Proc., Porto Alegre (Brazil) Symp., IAHS Publication No. 174, 337–350.
Wilby, R. L., Abrahart, R. J., and Dawson, C. W. (2003). “Detection of conceptual model rainfall-runoff processes inside an artificial neural network.” Hydrol. Sci. J., 48(2), 163–181.
Yen, B. C., and Gonzalez-Castro, J. A. (2000). “Open channel capacity determination using hydraulic performance graph.” J. Hydraul. Eng., 126(2), 112–122.
Zealand, C. M., Burn, D. H., and Simonovic, S. P. (1999). “Short term stream flow forecasting using artificial neural networks.” J. Hydrol., 214, 32–48.
Zhang, B., and Govindaraju, R. S. (2000). “Prediction of watershed runoff using Bayesian concepts and modular neural networks.” Water Resour. Res., 36(3), 753–762.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 13 • Issue 3 • March 2008
Pages: 124 - 131
Copyright
© 2008 ASCE.
History
Received: Mar 22, 2006
Accepted: Apr 5, 2007
Published online: Mar 1, 2008
Published in print: Mar 2008
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.