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Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 794061, 13 pages
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.
- 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.
- A. K. Mishra and V. P. Singh, “A review of drought concepts,” Journal of Hydrology, vol. 391, no. 1-2, pp. 202–216, 2010.
- 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.
- 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.
- 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.
- 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.
- 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.
- W. Palmer, “Meteorological drought,” Tech. Rep. 45, U.S. Weather Bureau, Washington, DC, USA, 1965.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- S. N. Lane, “Assessment of rainfall-runoff models based upon wavelet analysis,” Hydrological Processes, vol. 21, no. 5, pp. 586–607, 2007.
- 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.
- 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.
- 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.
- 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.
- 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.
- V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.
- 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.
- 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.
- 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.
- O. Kisi and M. Cimen, “Evapotranspiration modelling using support vector machines,” Hydrological Sciences Journal, vol. 54, no. 5, pp. 918–928, 2009.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- H. C. S. Thom, “A note on gamma distribution,” Monthly Weather Review, vol. 86, pp. 117–122, 1958.
- D. C. Edwards and T. B. McKee, “Characteristics of 20th century drought in the United States at multiple scales,” Atmospheric Science Paper 634, 1997.
- D. S. Wilks, Statistical Methods in the Atmospheric Sciences an Introduction, Academic Press, San Diego, Calif, USA, 1995.
- M. Abramowitz and A. Stegun, Eds., Handbook of Mathematical Formulas, Graphs, and Mathematical Tables, Dover Publications, New York, NY, USA, 1965.
- 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.
- 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.
- M. Çimen, “Estimation of daily suspended sediments using support vector machines,” Hydrological Sciences Journal, vol. 53, no. 3, pp. 656–666, 2008.
- A. J. Smola, Regression Estimation with Support Vector Learning Machines [M.S. thesis], Technische Universitat Munchen, Munich, Germany, 1996.
- S. Gunn, “Support vector machines for classification and regression,” ISIS Technical Report, Department of Electronics and Computer Science, University of Southampton, 1998.
- 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.
- S. G. Mallat, A Wavelet Tour of Signal Processing, Academic Press, San Diego, Calif, USA, 1998.
- F. Murtagh, J. L. Starck, and O. Renuad, “On neuro-wavelet modeling,” Decision Support Systems, vol. 37, no. 4, pp. 475–484, 2004.
- O. Renaud, J. Starck, and F. Murtagh, Wavelet-Based Forecasting of Short and Long Memory Time Series, Department of Economics, University of Geneve, 2002.
- 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.
- 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.
- 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.
- 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.
- J. C. Principe, N. R. Euliano, and W. Curt Lefebvre, Neural and Adaptive Systems, John Wiley & Sons, 2000.
- T. Partal, “Modelling evapotranspiration using discrete wavelet transform and neural networks,” Hydrological Processes, vol. 23, no. 25, pp. 3545–3555, 2009.
- F. Parrella, Online support vector regression [M.S. thesis], University of Genoa, 2007.