About this Journal Submit a Manuscript Table of Contents
Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 794061, 13 pages
http://dx.doi.org/10.1155/2012/794061
Research Article

Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression

Department of Bioresource Engineering, Faculty of Agricultural and Environmental Sciences, McGill University, QC, Canada H9X 3V9

Received 24 February 2012; Accepted 18 July 2012

Academic Editor: Quek Hiok Chai

Copyright © 2012 A. Belayneh and J. Adamowski. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. E. Mersha and V. K. Boken, “Agricultural drought in Ethiopia,” in Monitoring and Predicting Agricultural Drought: A Global Study, V. K. Boken, A. P. Cracknell, and R. L. Heathcote, Eds., Oxford University Press, 2005.
  2. A. K. Mishra and V. P. Singh, “A review of drought concepts,” Journal of Hydrology, vol. 391, no. 1-2, pp. 202–216, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. T. Ross and N. Lott, “A climatology of 1980–2003 extreme weather and climate events,” National Climatic Data Center Technical Report No. 2003-01. NOAA/ NESDIS, National Climatic Data Center, Asheville, NC, USA.
  4. A. Cancelliere, G. di Mauro, B. Bonaccorso, and G. Rossi, “Stochastic forecasting of drought indices,” in Methods and Tools For Drought Analysis and Management, G. Rossi, T. Vega, and B. Bonaccorso, Eds., Springer, 2007.
  5. W. J. Gibbs and J. V. Maher, Rainfall Deciles as Drought Indicators, vol. 48 of Bulletin (Commonwealth Bureau of Meteorology, Australia), Bureau of Meteorology, Melbourne, Australia, 1967.
  6. T. B. McKee, N. J. Doesken, and J. Kleist, “The relationship of drought frequency and duration to time scales,” in Proceedings of the 8th Conference on Applied Climatology, American Meteorological Society, Anaheim, Calif, USA, 1993.
  7. H. R. Byun and D. A. Wilhite, “Objective quantification of drought severity and duration,” Journal of Climate, vol. 12, no. 9, pp. 2747–2756, 1999. View at Scopus
  8. W. Palmer, “Meteorological drought,” Tech. Rep. 45, U.S. Weather Bureau, Washington, DC, USA, 1965.
  9. H. K. Ntale and T. Y. Gan, “Drought indices and their application to East Africa,” International Journal of Climatology, vol. 23, no. 11, pp. 1335–1357, 2003. View at Publisher · View at Google Scholar · View at Scopus
  10. A. K. Mishra and V. R. Desai, “Drought forecasting using feed-forward recursive neural network,” Ecological Modelling, vol. 198, no. 1-2, pp. 127–138, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Morid, V. Smakhtin, and K. Bagherzadeh, “Drought forecasting using artificial neural networks and time series of drought indices,” International Journal of Climatology, vol. 27, no. 15, pp. 2103–2111, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. U. G. Bacanli, M. Firat, and F. Dikbas, “Adaptive Neuro-Fuzzy inference system for drought forecasting,” Stochastic Environmental Research and Risk Assessment, vol. 23, no. 8, pp. 1143–1154, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. A. P. Barros and G. J. Bowden, “Toward long-lead operational forecasts of drought: an experimental study in the Murray-Darling River Basin,” Journal of Hydrology, vol. 357, no. 3-4, pp. 349–367, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. P. Cutore, G. Di Mauro, and A. Cancelliere, “Forecasting palmer index using neural networks and climatic indexes,” Journal of Hydrologic Engineering, vol. 14, no. 6, pp. 588–595, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Karamouz, K. Rasouli, and S. Nazif, “Development of a hybrid Index for drought prediction: case study,” Journal of Hydrologic Engineering, vol. 14, no. 6, pp. 617–627, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. A. F. Marj and A. M. J. Meijerink, “Agricultural drought forecasting using satellite images, climate indices and artificial neural network,” International Journal of Remote Sensing, vol. 32, no. 24, pp. 9707–9719, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. D. Labat, R. Ababou, and A. Mangin, “Wavelet analysis in karstic hydrology. 2nd part: rainfall-runoff cross-wavelet analysis,” Comptes Rendus de l'Academie de Sciences, vol. 329, no. 12, pp. 881–887, 1999. View at Publisher · View at Google Scholar · View at Scopus
  18. P. Saco and P. Kumar, “Coherent modes in multiscale variability of streamflow over the United States,” Water Resources Research, vol. 36, no. 4, pp. 1049–1067, 2000. View at Publisher · View at Google Scholar · View at Scopus
  19. L. C. Smith, D. L. Turcotte, and B. L. Isacks, “Stream flow characterization and feature detection using a discrete wavelet transform,” Hydrological Processes, vol. 12, no. 2, pp. 233–249, 1998. View at Scopus
  20. P. Coulibaly, F. Anctil, and B. Bobée, “Daily reservoir inflow forecasting using artificial neural networks with stopped training approach,” Journal of Hydrology, vol. 230, no. 3-4, pp. 244–257, 2000. View at Publisher · View at Google Scholar · View at Scopus
  21. S. N. Lane, “Assessment of rainfall-runoff models based upon wavelet analysis,” Hydrological Processes, vol. 21, no. 5, pp. 586–607, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. J. F. Adamowski, “Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis,” Journal of Hydrology, vol. 353, no. 3-4, pp. 247–266, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Adamowski and K. Sun, “Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds,” Journal of Hydrology, vol. 390, no. 1-2, pp. 85–91, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Özger, A. K. Mishra, and V. P. Singh, “Long lead time drought forecasting using a wavelet and fuzzy logic combination model: a case study in Texas,” Journal of Hydrometeorology, vol. 13, no. 1, pp. 284–297, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. T. Partal and Ö. Kişi, “Wavelet and neuro-fuzzy conjunction model for precipitation forecasting,” Journal of Hydrology, vol. 342, no. 1-2, pp. 199–212, 2007. View at Publisher · View at Google Scholar · View at Scopus
  26. T. W. Kim and J. B. Valdes, “Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks,” Journal of Hydrologic Engineering, vol. 8, no. 6, pp. 319–328, 2003. View at Publisher · View at Google Scholar · View at Scopus
  27. V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.
  28. J. B. Gao, S. R. Gunn, C. J. Harris, and M. Brown, “A probabilistic framework for SVM regression and error bar estimation,” Machine Learning, vol. 46, no. 1–3, pp. 71–89, 2002. View at Publisher · View at Google Scholar · View at Scopus
  29. M. S. Khan and P. Coulibaly, “Application of support vector machine in lake water level prediction,” Journal of Hydrologic Engineering, vol. 11, no. 3, pp. 199–205, 2006. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Rajasekaran, S. Gayathri, and T.-L. Lee, “Support vector regression methodology for storm surge predictions,” Journal of Ocean Engineering, vol. 35, no. 16, pp. 1578–1587, 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. O. Kisi and M. Cimen, “Evapotranspiration modelling using support vector machines,” Hydrological Sciences Journal, vol. 54, no. 5, pp. 918–928, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. O. Kisi and M. Cimen, “A wavelet-support vector machine conjunction model for monthly streamflow forecasting,” Journal of Hydrology, vol. 399, no. 1-2, pp. 132–140, 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. T. Asefa, M. Kemblowski, M. McKee, and A. Khalil, “Multi-time scale stream flow predictions: the support vector machines approach,” Journal of Hydrology, vol. 318, no. 1–4, pp. 7–16, 2006. View at Publisher · View at Google Scholar · View at Scopus
  34. W. C. Wang, K. W. Chau, C. T. Cheng, and L. Qiu, “A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series,” Journal of Hydrology, vol. 374, no. 3-4, pp. 294–306, 2009. View at Publisher · View at Google Scholar · View at Scopus
  35. R. Maity, P. P. Bhagwat, and A. Bhatnagar, “Potential of support vector regression for prediction of monthly streamflow using endogenous property,” Hydrological Processes, vol. 24, no. 7, pp. 917–923, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. Z. M. Yuan and X. S. Tan, “Nonlinear screening indicators of drought resistance at seedling stage of rice based on support vector machine,” Acta Agronomica Sinica, vol. 36, no. 7, pp. 1176–1182, 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. C. Cacciamani, A. Morgillo, S. Marchesi, and V. Pavan, “Monitoring and forecasting drought on a regional scale: emilia-romagna region,” Water Science and Technology Library, vol. 62, part 1, pp. 29–48, 2007. View at Publisher · View at Google Scholar
  38. I. Bordi and A. Sutera, “Drought monitoring and forecasting at large-scale,” in Methods and Tools For Drought Analysis and Management, G. Rossi, T. Vega, and B. Bonaccorso, Eds., pp. 3–27, Springer, New York, NY, USA, 2007.
  39. N. B. Guttman, “Accepting the standardized precipitation index: a calculation algorithm,” Journal of the American Water Resources Association, vol. 35, no. 2, pp. 311–322, 1999. View at Scopus
  40. H. C. S. Thom, “A note on gamma distribution,” Monthly Weather Review, vol. 86, pp. 117–122, 1958.
  41. D. C. Edwards and T. B. McKee, “Characteristics of 20th century drought in the United States at multiple scales,” Atmospheric Science Paper 634, 1997.
  42. D. S. Wilks, Statistical Methods in the Atmospheric Sciences an Introduction, Academic Press, San Diego, Calif, USA, 1995.
  43. M. Abramowitz and A. Stegun, Eds., Handbook of Mathematical Formulas, Graphs, and Mathematical Tables, Dover Publications, New York, NY, USA, 1965.
  44. S. Morid, V. Smakhtin, and M. Moghaddasi, “Comparison of seven meteorological indices for drought monitoring in Iran,” International Journal of Climatology, vol. 26, no. 7, pp. 971–985, 2006. View at Publisher · View at Google Scholar · View at Scopus
  45. J. Adamowski and H. F. Chan, “A wavelet neural network conjunction model for groundwater level forecasting,” Journal of Hydrology, vol. 407, no. 1–4, pp. 28–40, 2011. View at Publisher · View at Google Scholar · View at Scopus
  46. M. Çimen, “Estimation of daily suspended sediments using support vector machines,” Hydrological Sciences Journal, vol. 53, no. 3, pp. 656–666, 2008. View at Publisher · View at Google Scholar · View at Scopus
  47. A. J. Smola, Regression Estimation with Support Vector Learning Machines [M.S. thesis], Technische Universitat Munchen, Munich, Germany, 1996.
  48. S. Gunn, “Support vector machines for classification and regression,” ISIS Technical Report, Department of Electronics and Computer Science, University of Southampton, 1998.
  49. B. Cannas, A. Fanni, G. Sias, S. Tronci, and M. K. Zedda, “River flow forecasting using neural networks and wavelet analysis,” in Proceedings of the European Geosciences Union, 2006.
  50. S. G. Mallat, A Wavelet Tour of Signal Processing, Academic Press, San Diego, Calif, USA, 1998.
  51. F. Murtagh, J. L. Starck, and O. Renuad, “On neuro-wavelet modeling,” Decision Support Systems, vol. 37, no. 4, pp. 475–484, 2004. View at Publisher · View at Google Scholar · View at Scopus
  52. O. Renaud, J. Starck, and F. Murtagh, Wavelet-Based Forecasting of Short and Long Memory Time Series, Department of Economics, University of Geneve, 2002.
  53. C. E. Desalegn, M. S. Babel, A. Das Gupta, B. A. Seleshi, and D. Merrey, “Farmers' perception of water management under drought conditions in the upper Awash Basin, Ethiopia,” International Journal of Water Resources Development, vol. 22, no. 4, pp. 589–602, 2006. View at Publisher · View at Google Scholar · View at Scopus
  54. D. C. Edossa, M. S. Babel, and A. D. Gupta, “Drought analysis in the Awash River Basin, Ethiopia,” Water Resources Management, vol. 24, no. 7, pp. 1441–1460, 2010. View at Publisher · View at Google Scholar · View at Scopus
  55. M. K. Tiwari and C. Chatterjee, “Development of an accurate and reliable hourly flood forecasting model using wavelet-bootstrap-ANN (WBANN) hybrid approach,” Journal of Hydrology, vol. 394, no. 3-4, pp. 458–470, 2010. View at Publisher · View at Google Scholar · View at Scopus
  56. N. Wanas, G. Auda, M. S. Kamel, and F. Karray, “On the optimal number of hidden nodes in a neural network,” in Proceedings of the 11th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE '98), pp. 918–921, May 1998. View at Scopus
  57. J. C. Principe, N. R. Euliano, and W. Curt Lefebvre, Neural and Adaptive Systems, John Wiley & Sons, 2000.
  58. T. Partal, “Modelling evapotranspiration using discrete wavelet transform and neural networks,” Hydrological Processes, vol. 23, no. 25, pp. 3545–3555, 2009. View at Publisher · View at Google Scholar · View at Scopus
  59. F. Parrella, Online support vector regression [M.S. thesis], University of Genoa, 2007.