Nonparametric methods for drought severity estimation at ungauged sites

Water Resources Research

Volumn 48, Issue 12, 2012, Pages

  Sadri, S ^a   Burn, D H ^b  

^a Princeton University   (United States)

^b UNIVERSITY OF WATERLOO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CANADIAN PRAIRIE; DROUGHT FREQUENCY ANALYSIS; DROUGHT SEVERITY; EXTREME EVENTS; FREQUENCY ANALYSIS; FUNCTION ESTIMATION; LEAST SQUARES SUPPORT VECTOR REGRESSION; MULTIPLE LINEAR REGRESSIONS; NONPARAMETRIC METHODS; QUANTILE ESTIMATION; RADIAL BASIS FUNCTION NEURAL NETWORKS; RETURN PERIODS; STATISTICAL MODELS;

CATCHMENTS; DROUGHT; ESTIMATION; FREQUENCY ESTIMATION; LINEAR REGRESSION; MODELS; NEURAL NETWORKS; RUNOFF;

LEAST SQUARES APPROXIMATIONS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CATCHMENT; DROUGHT STRESS; PRAIRIE; REGRESSION ANALYSIS;

CANADA;

EID: 84870952875     PISSN: 00431397     EISSN: 19447973     Source Type: Journal    
DOI: 10.1029/2011WR011323     Document Type: Article

References (26)

1. Ahmad, S., and S. P. Simonovic (2005), An artificial neural network model for generating hydrograph from hydro-meteorological parameters, J. Hydrol., 315, 236-251.
2. Apipattanavis, S., B. Rajagopalan, and U. Lall (2010), Local polynomial based flood frequency estimator for mixed population, J. Hydrol. Eng., 15, 680-691.
3. Asefa, T., M. Kemblowski, M. McKee, and A. Khalil (2006), Multi-time scale stream flow predictions: The support vector machine approach, J. Hydrol., 318, 7-16.
4. Bako, L., K. Boukharouba, E. Duviella, and S. Lecoeuche (2011), A recursive identification algorithm for switched linear/affine models, Nonlinear Anal. Hybrid Syst., 5, 242-253.
5. Dawson, C., R. Abrahart, A. Shamseldin, and R. Wilby (2006), Flood estimation at ungauged sites using artificial neural networks, J. Hydrol., 319, 391-409.
6. Ghodsi, A., and D. Schuurmans (2003), Automatic basis selection techniques for RBF networks, J. Neural Networks, 16(5-6), 809-816.
7. Ghosh, S. (2010), SVM-PGSL coupled approach for statistical downscaling to predict rainfall from GCM output, J. Geophys. Res., 115, D22102, doi:10.1029/2009JD013548.
8. Haghighatjou, P., A. Akhoond-Ali, A. Behnia, and R. Chinipardaz (2008), Parametric and nonparametric frequency analysis of monthly precipitation in Iran, J. Appl. Sci., 8(18), 3242-3248.
9. Hosking, J. R. M., and J. R. Wallis (1997), Regional Frequency Analysis: An Approach Based on L-Moments, 224 pp., Cambridge Univ. Press, New York.
10. Karray, F. O., and C. De Silva (2004), Soft Computing and Intelligent Systems Design, 560 pp., Pearson, Harlow, U. K.
11. Kjeldsen, T. R., A. Lundorf, and D. Rosbjerg (1999), Regional partial duration series modelling of hydrological droughts in Zimbabwean rivers using a two-component exponential distribution, IAHS Publ., 255, 145-153.
12. Kroll, C. N., and R. M. Vogel (2002), Probability distribution of low streamflow series in the United States, J. Hydrol. Eng., 7(2), 137-146.
13. Lefebvre, M. (2004), A bimodal model for the high values of a river flow, Can. J. Civ. Eng., 31, 473-477.
14. Lin, J.-Y., C.-T. Cheng, and K.-W. Chau (2006), Using support vector machines for long-term discharge prediction, Hydrol. Sci. J., 51(4), 599-612.
15. Moon, Y.-I., and U. Lall (1994), Kernel quantile function estimator for flood frequency analysis, Water Resour. Res., 30, 3095-3103.
16. Reed, D. W., and A. J. Robson (1999), Flood Estimation Handbook, vol. 3, Inst. of Hydrol., Wallingford, U. K.
17. Shu, C., and D. H. Burn (2004), Artificial neural network ensembles and their application in pooled flood frequency analysis, Water Resour. Res., 40, W09301, doi:10.1029/2003WR002816.
18. Shu, C., and T. Ouarda (2008), Regional flood frequency analysis at unga-uged sites using the adaptive neuro-fuzzy inference system, J. Hydrol., 349, 31-43.
19. Smola, A. J., and B. Schlkopf (2003), A tutorial on support vector regression, technical report, Res. Sch. of Inf. Sci. and Eng., Aust. Natl. Univ., Canberra.
20. Suykens, J. A., T. V. Gestel, J. D. Brabanter, B. D. Moor, and J. Vandewalle (2002), Least Squares Support Vector Machines, World Sci., Singapore.
21. Suykens, J., T. V. Gestel, J. D. Brabanter, B. D. Moor, and J. Vandewalle (2011), LS-SVM lab, software, [Available at http://www.esat.kuleu-ven.be/sista/ lssvmlab/], KU Leuven, Leuven, Belgium.
22. Tripathi, S., V. Srinivas, and R. S. Nanjundiah (2006), Downscaling of precipitation for climate change scenarios: A support vector machine approach, J. Hydrol., 330, 621-640.
23. Vapnik, V. (1995), The Nature of Statistical Learning Theory, Springer, New York.
24. Vapnik, V. (2006), Estimation of Dependencies Based on Empirical Data, Springer, New York.
25. Yevjevich, V. (1967), An objective approach to definitions and investigations of continental hydrologic droughts, Hydrol. Pap. 23, Colo. State Univ., Fort Collins.
26. Zrinji, Z., and D. H. Burn (1994), Flood frequency analysis for ungauged sites using a region of influence approach, J. Hydrol., 153(1-4), 1-21.