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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.
Citations to this Article [6 citations]
The following is the list of published articles that have cited the current article.
- Joo-Heon Lee, Jong-Suk Kim, Ho-Won Jang, and Jang-Choon Lee, “Drought Forecasting Using the Multi Layer Perceptron (MLP) Artificial Neural Network Model,” Journal of Korea Water Resources Association, vol. 46, no. 12, pp. 1249–1263, 2013.
- A. Belayneh, J. Adamowski, B. Khalil, and B. Ozga-Zielinski, “Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural networks and wavelet support vector regression models,” Journal of Hydrology, 2013.
- Ali Danandeh Mehr, Ercan Kahya, and Mehmet Ozger, “A gene-wavelet model for long lead time drought forecasting,” Journal of Hydrology, vol. 517, pp. 691–699, 2014.
- Vu Minh Tue, Srivatsan V. Raghavan, Pham Duc Minh, and Liong Shie-Yui, “Investigating drought over the central highland, vietnam, using regional climate models,” Journal of Hydrology, 2014.
- Ravinesh C. Deo, and Mehmet Şahin, “Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia,” Atmospheric Research, 2014.
- A. Belayneh, J. Adamowski, B. Khalil, and B. Ozga-Zielinski, “Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models,” Journal of Hydrology, vol. 508, pp. 418–429, 2014.