Estimation of daily suspended sediments using support vector machines

Hydrological Sciences Journal

Volumn 53, Issue 3, 2008, Pages 656-666

  Çimen, Mesut ^a  

^a SULEYMAN DEMIREL UNIVERSITY   (Turkey)

Author keywords

Kernel functions; Modelling; Regression analysis; Support vector machines; Suspended sediment concentration load

Indexed keywords

COMPUTATIONAL METHODS; ESTIMATION; FOOD PROCESSING; IMAGE RETRIEVAL; KETONES; LEARNING SYSTEMS; MOBILE TELECOMMUNICATION SYSTEMS; REUSABILITY; SEDIMENTOLOGY; SOFT COMPUTING; SUPPORT VECTOR MACHINES; VECTORS; WATER RESOURCES;

APPLIED (CO); REGRESSION (R2); SOFT COMPUTING TECHNIQUES; STREAM FLOWS; SUSPENDED SEDIMENTS; VECTOR MACHINES;

SEDIMENTATION;

ARTIFICIAL INTELLIGENCE; ESTIMATION METHOD; FLUVIAL DEPOSIT; MODELING; REGRESSION ANALYSIS; SUSPENDED SEDIMENT;

EID: 45449119903     PISSN: 02626667     EISSN: None     Source Type: Journal    
DOI: 10.1623/hysj.53.3.656     Document Type: Article

References (26)

1. Asselman, N. E. M. (2000) Fitting and interpretation of sediment rating curves. J. Hydrol. 234, 228-248.
2. Ciǧizoǧlu, H. K. (2004) Estimation and forecasting of daily suspended sediment data by multi layer perceptrons. Adv. Water Resour. 27, 185-195.
3. Ciǧizoǧlu, H. K. & Kişi, Ö. (2006) Methods to improve the neural network performance in suspended sediment estimation. J. Hydrol. 317, 221-238.
4. Cohn, T. A., Caulder, D. L., Gilroy, E. J., Zynjuk, L. D. & Summers, R. M. (1992) The validity of a simple statistical model for estimating fluvial constituent loads: An empirical study involving nutrient loads entering Chesapeake Bay. Water Resour. Res. 28, 2353-2363.
5. Ferguson, R. I. (1986) River loads underestimated by rating curves. Water Resour. Res. 22, 74-76.
6. Ferguson, R. I. (1987) Accuracy and precision of methods for estimating river loads. Earth Surf. Processes Landf. 12, 95-104.
7. Gao, J. B., Gunn, S. R., Harris, C. J. & Brown, M. (2001) A probabilistic framework for SVM regression and error bar estimation. Machine Learning 46, 71-89.
8. Gunn, S. R. (1998) Support vector machines for classification and regression. Tech. Report, University of Southampton, UK.
9. Khan, M. S. & Coulibaly, P. (2006) Application of support vector machine in lake water level prediction. J. Hydrol. Engng 11(3), 199-205.
10. Kişi, Ö. (2004a) Daily suspended sediment modelling using a fuzzy differential evolution approach. Hydrol Sci. J. 49(1), 183-197.
11. Kişi, Ö. (2004b) Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment prediction and estimation. Hydrol. Sci. J. 49(6), 1025-1040.
12. Kişi, Ö. (2005) Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrol. Sci. J. 50(4), 683-696.
13. Kişi, Ö., Karahan, M. E. & Şen, Z. (2006) River suspended sediment modeling using a fuzzy logic approach. Hydrol. Processes 20, 4351-4362.
14. Morgan, R. P. C. (1995) Soil Erosion and Conservation. Longman, London, UK.
15. Nash, J. E. & Sutcliffe, J. V. (1970) River flow forecasting through conceptual models. Part I: A discussion of principles. J. Hydrol. 10, 282-290.
16. Peters-Kümmerly, B. E. (1973) Untersuchungen über Zusammensetzung und Transport von Schwebstoffen in einigen Schweizer Flüssen. Geographica Helvetica 28, 137-151.
17. Quilbé, R., Rousseau, A. N., Duchemin, M., Poulin, A., Gangbazo, G. & Villeneuve, J.-P. (2006) Selecting a calculation method to estimate sediment and nutrient loads in streams: application to the Beaurivage River (Québec, Canada). J. Hydrol. 326, 295-310.
18. Singh, K. P. & Durgunoǧlu, A. (1989) Developing accurate and reliable stream sediment yields. In: Sediment and the Environment (Proc. Baltimore Symp.) (ed. by R. F. Hadley & E. D. Ongley), 193-199. IAHS Publ. 184, IAHS Press, Wallingford, UK.
19. Sivapragasam, C. & Muttil, N. (2005) Discharge rating curve extension: A new approach. Water Resour. Manage. 19(5), 505-520.
20. Smola, A. J. (1996) Regression estimation with Support Vector Learning Machines. MSc Thesis, Technische Universität München, Germany.
21. Tayfur, G. (2002) Artificial neural networks for sheet sediment transport. Hydrol. Sci. J. 47(6), 879-892.
22. Tayfur, G., Ozdemir, S. & Singh, V. P. (2003) Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces. Adv. Water Resour. 26, 1249-1256.
23. Tayfur G. & Guldal V. (2006) Artificial neural networks for estimating daily total suspended sediment in natural streams. Nordic Hydrol. 37(1), 69-79.
24. Vapnik, V. (1995) The Nature of Statistical Learning Theory. Springer Verlag, New York, USA.
25. Vapnik, V., Golwich, S. & Smola, A. J. (1997) Support vector method for function approximation, regression estimation, and signal processing. In: Advances in Neural Information Processing Systems 9 (ed. by M. Mozer, M. Jordan & T. Petsche), 281-287. MIT Press, Cambridge, Massachusetts, USA.
26. Walling, D. E. (1977) Assessing the accuracy of suspended sediment rating curves for a small basin. Water Resour. Res. 13, 531-538.