Stage–Discharge Relations for Low-Gradient Tidal Streams Using Data-Driven Models
Publication: Journal of Hydraulic Engineering
Volume 132, Issue 5
Abstract
Development of stage–discharge relationships for coastal low-gradient streams is a challenging task. Such relationships are highly nonlinear, nonunique, and often exhibit multiple loops. Conventional parametric regression methods usually fail to model these relationships. Therefore, this study examines the utility of two data-driven computationally intensive modeling techniques namely, artificial neural networks and local nonparametric regression, to model such complex relationships. The results show an overall good performance of both modeling techniques. Both neural network and local regression models are able to predict and reproduce the stage–discharge multiple loops that are observed at the outlet of a low-gradient subcatchment in southwestern Louisiana. However, the neural network model is characterized with higher prediction ability for most of the tested runoff events. In agreement with the physical characteristics of low-gradient streams, the results indicate the importance of including information about downstream and upstream water levels, in addition to water level at the prediction site.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This study was partially supported by the Army Research Office Grant No. USARDDAAD19-00-1-0413 under the supervision of Dr. Russell Harmon, and by the Louisiana Transportation Research Center, Project No. UNSPECIFIED736-99-0918 under the supervision of Dr. Chester Wilmot. The writers would like to thank Ms. Tzvetlena Peeva and Ms. Sarada Kalikivaya for their help in data collection and developing some of the computer codes used in the study.
References
Abrahart, R. J. (2003). “Neural network rainfall-runoff forecasting based on continuous resampling.” J. Hydroinformatics, 5, 51–61.
American Society of Civil Engineers (ASCE) Task Committee on Artificial Neural Networks in Hydrology. (2000a). “Artificial neural networks in hydrology. I: Preliminary concepts.” J. Hydrologic Eng., 5(2), 115–123.
American Society of Civil Engineers (ASCE) Task Committee on Artificial Neural Networks in Hydrology. (2000b). “Artificial neural networks in hydrology. II: Hydrologic applications.” J. Hydrologic Eng., 5(2), 124–137.
Bhattacharya, B., and Solomatine, D. P. (2000). “Application of artificial neural network in stage-discharge relationship.” Proc., 4th Int. Conf. on Hydroinformatics, IAHR, Iowa City, Iowa.
Bishop, C. M. (1994). “Neural networks and their applications.” Rev. Sci. Instrum., 65, 1803–1832.
Bowden, G. J., Dandy, G. C., and Maier, H. R. (2005). “Input determination for neural network models in water resources applications. Part 1—Background and methodology.” J. Hydrol., 302, 75–92.
Bowden, G. J., Maier, H. R., and Dandy, G. C. (2002). “Optimal division of data for neural network models in water resources applications.” Water Resour. Res., 38(2), 2.1–2.11.
Cleveland, W. S. (1979). “Robust locally weighted regression and smoothing scatter plots.” J. Am. Stat. Assoc., 74, 829–836.
Cleveland, W. S., Grosse, E., and Shyu, W. M. (1992). “Local regression models.” Statistical models in S, J. M. Chambers and T. Hastie, eds., Chapman and Hall, New York, 309–376.
Cleveland, W. S., and Loader, C. L. (1996). “Smoothing by local regression: Principles and methods.” Statistical theory and computational aspects of smoothing, W. Haerdle and M. G. Schimek, eds., Springer, New York, 10–49.
Fread, D. L. (1973). “A dynamic model of stage-discharge relations affected by changing discharge.” NOAA Tech. Memo. NWS HYDRO-16, National Weather Service, Silver Spring, Md.
Fread, D. L. (1975). “Computation of stage-discharge relationships affected by unsteady flow.” Water Resour. Bull., 11(2), 213–228.
Hagan, M., Demuth, H., and Beale, M. (1996). Neural network design, PWS, Boston.
Hagan, M. T., and Menhaj, M. B. (1994). “Training feedforward networks with the Marquardt algorithm.” IEEE Trans. Neural Netw., 5(6), 989–993.
Hardle, W. (1990). Applied nonparametric regression, Cambridge University Press, Cambridge, U.K.
Hsu, K.-L., Gupta, H. V., and Sorooshian S. (1995). “Artificial neural network modeling of the rainfall-runoff process.” Water Resour. Res., 31(10), 2517–2530.
Jain, S. K., and Chalisgaonkar, D. (2000). “Setting up stage-discharge relations using ANN.” J. Hydrologic Eng., 5(4), 428–433.
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.
Maier, H. R., and Dandy, G. C. (2000). “Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications.” Environmental Modelling and Software, 15(1), 101–124.
Masters, T. (1993). Practical neural network recipes in C++, Academic, New York.
McCuen, R. H. (2005). “Accuracy assessment of peak discharge models.” J. Hydrologic Eng., 10(1), 16–22.
Schmidt, A. R., and Yen, B. C. (2002). “Stage-discharge ratings revisited.” Hydraulic Measurements and Experimental Methods, Proc., EWRI and IAHR Joint Conf., (CD-Rom), T. L. Wahl, C. A. Pugh, K. A. Oberg, and T. B. Vermeyen, eds., Estes Park, Colo.
Stone, M. (1974). “Cross-validatory choice and assessment of statistical predictions.” J. R. Stat. Soc. Ser. B. Methodol., 36, 111–147.
Sudheer, K. P., and Jain, S. K. (2003). “Radial basis function neural network for modeling rating curves.” J. Hydrologic Eng., 8(3), 161–164.
Supharatid, S. (2003). “Application of a neural network model in establishing a stage-discharge relationship for a tidal river.” Hydrolog. Process., 17(15), 3085–3099.
Tawfik, M., Ibrahim, A., and Fahmy, H. (1997). “Hysteresis sensitive neural network for modeling rating curves.” J. Comput. Civ. Eng., 11(3), 206–211.
Information & Authors
Information
Published In
Journal of Hydraulic Engineering
Volume 132 • Issue 5 • May 2006
Pages: 482 - 492
Copyright
© 2006 ASCE.
History
Received: May 24, 2004
Accepted: Jul 8, 2005
Published online: May 1, 2006
Published in print: May 2006
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.