Application and analysis of support vector machine based simulation for runoff and sediment yield

Biosystems Engineering

Volumn 103, Issue 4, 2009, Pages 527-535

  Misra, Debasmita ^a   Oommen, Thomas ^b   Agarwal, Avinash ^c   Mishra, Surendra K ^d   Thompson, Anita M ^e  

^a University of Alaska Fairbanks   (United States)

^b Tufts University   (United States)

^c NATIONAL INSTITUTE OF HYDROLOGY   (India)

^d INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

^e University of Wisconsin Madison   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CALIBRATION; EFFICIENCY; PATTERN RECOGNITION; RUNOFF; SEDIMENTS;

CALIBRATION AND VALIDATIONS; COEFFICIENT OF EFFICIENCIES; CORRELATION COEFFICIENT; PATTERN RECOGNITION ALGORITHMS; PATTERN RECOGNITION TECHNIQUES; PHYSICS-BASED MODELS; RUNOFF AND SEDIMENT YIELDS; RUNOFF ESTIMATION;

SUPPORT VECTOR MACHINES;

EID: 67650621579     PISSN: 15375110     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biosystemseng.2009.04.017     Document Type: Article

References (23)

1. Ardiclioglu M., Kisi O., and Haktanir T. Suspended sediment prediction by using two different feed-forward back-propagation algorithms. Canadian Journal of Civil Engineering 34 1 (2007) 120-125
2. Agarwal A (2002). Artificial Neural Networks and their application for simulation and prediction of runoff and sediment yield. Ph.D. thesis, Department of soil and Water Conservation Engineering. G.B. Pant University of Agriculture and Technology, Pantnagar, India.
3. Agarwal A., Mishra S.K., Ram S., and Singh J.K. Simulation of runoff and sediment yield using artificial neural networks. Biosystems Engineering 94 4 (2006) 597-613
4. Cimen M. Estimation of daily suspended sediments using support vector machines. Hydrological Science Journal 53 3 (2008) 656-666
5. Hastie T., Tibshirani R., and Friedman J. The Elements of Statistical Learning: Data Mining, Inference and Prediction (2003), Springer-Verlag, New York. 0-387-95284-5 533p
6. Kecman V. Learning and Soft Computing, Support Vector Machines, Neural Network and Fuzzy Logic Models (2000), MIT Press. 0-262-11255-8 608p
7. Kessler E., and Neas B. On correlation, with applications to the radar and raingage measurement of rainfall. Atmospheric Research 34 (1994) 217-229
8. Keerthi S.S., and Lin C.J. Asymptotic behaviors of support vector machines with Gaussian Kernel. Neural Computation 15 (2003) 1667-1689
9. Legates D.R., and Davis R.E. The continuing search for an anthropogenic climate change signal - limitations of correlation-based approaches. Geophysical Research Letters 24 18 (1997) 2319-2322
10. Legates D.R., and McCabe G.J. Evaluating the use of "Goodness of Fit" measures in hydrologic and hydroclimatic model validation. Water Resources Research 35 1 (1999) 233-241
11. Nash J.E., and Sutcliffe J.V. River flow forecasting through conceptual models. Journal of Hydrology 10 (1970) 282-290
12. Oommen T., Prakash A., Misra D., Kelley J.J., Naidu S., and Bandopadhyay S. GIS based marine platinum exploration, goodnews bay, southwest Alaska. Marine Georesources & Geotechnology 26 1 (2008) 1-18
13. Oommen T., Misra D., Twarakavi N.K.C., Prakash A., Sahoo B., and Bandopadhyay S. An objective analysis of support vector machine based classification for remote sensing. Mathematical Geosciences 40 4 (2008) 409-424
14. Oommen T; Misra D; Prakash A; Bandopadhyay S; Naidu S; Kelley J J (2006) Marine Geodatabase and Multiple Regressive Pattern Recognition Technique: A New Approach To Marine Placer Resource Assessment. American Geophysical Union Fall Annual Meeting, San Francisco, CA, Poster (IN14A).
15. Rajurkar M.P., Kothyari U.C., and Chaube U.C. Modeling of the daily rainfall-runoff relationship with artificial neural network. Journal of Hydrology 285 (2004) 96-113
16. Raghuwanshi N.S., Singh R., and Reddy L.S. Runoff and sediment yield modeling using artificial neural networks: upper siwane river, India. Journal of Hydrologic Engineering 1 71 (2006) 10.1061/(ASCE)1084-0699(2006)11
17. Smola A.J., and Schölkopf B. A tutorial on support vector regression. Statistics and Computing 14 (2004) 199-222
18. Twarakavi N.K., Misra D., and Bandopadhyay S. Prediction of arsenic in bedrock derived stream sediments at a gold mine site under conditions of sparse data. Natural Resources Research (2006) 10.1007/s11053-006-9013-6
19. Vapnik V.N. The Nature of Statistical Learning Theory (1995), Springer, New York
20. Vapnik V.N. Statistical Learning Theory (1998), John Wiley and Sons, New York
21. Willmott C.J. On the validation of models. Physical Geography 2 (1981) 184-194 p. 179
22. Willmott C.J., Ackleson S.G., Davis R.E., Feddema J.J., Klink K.M., Legates D.R., O'Donnell J., and Rowe C.M. Statistics for the evaluation and comparison of models. Journal of Geophysical Research 90 (1985) 8995-9005
23. Wolpert D.H. Stacked generalization. Neural Networks 5 (1992) 241-259