1- Ebrahimi, R., Zahraie, B., and Nasseri, M. (2011). “Mid-term prediction of metorological drought using fuzzy inference system.” J. of Water and Wastewater, 78, 112-125 (In Persian)
2- Nazemossadat, M.J. (1998). “Persian Gulf sea surface tempreture as a drought diagnostic for southern parts of Iran.” J. of Drought News Network, 10, 12-14.
3- Nazemossadat, M.J., and Cordery I., (2000). “On the relationship between ENSO and autumn rainfall in Iran.” International J. of Climatology, 1, 42-67.
4- Karamouz, M., Fallahi, M., Nazif, S., and Rahimi Farahani, M. (2009). “Long lead rainfall prediction using statistical downscaling and artificial neural network modeling.” Scintia Iranica, 16, 165-172.
5- Zahraie, B., and Karamouz, M. (2004). “Seasonal precipitation prediction using large scale climate signals.” Proceedings of EWRI-2004 Conference, Salt lake City, USA.
6- Loukas A., and Vasiliades, L. (2004). “Probabilistic analysis of drought spatiotemporal characteristics in thessaly region.” Greece. Natural Hazards and Earth System Sciences, 4, 719-731.
7- Paulo, A. A., Ferreira, E., Coelho, C., and Pereira, L.S. (2005). “Drought class transition analysis through Markov and loglinear models, an approach to early warning.” Agricultural Water Management, 77, 59-81.
8- Moreira, E.E., Paulo, A.A., Pereira, L.S., and Mexia, J.T. (2006). “Analysis of SPI drought class transitions using loglinear models.” J. Hydrol., 331, 349-359.
9- Zahraie, B., and Roozbahani, A., (2007). “Climate signal clustering using genetic algorithm for precipitation forecasting: A case study of southeast of Iran.” Proceedings of the Word Environmental and Water Resources Congress (ASCE), Tampa, Florida, USA.
10- Vapnik, V. N., and Cortes, C. (1995). “Support vector networks.” Machine Learning, 20, 273-297.
11- Liong, S.-Y., and Sivapragasam, C. (2002). “Flood stage forecasting with support vector machines.” J. of the American Water Resources Association, 38 (1), 173-196.
12- Choy, K.Y., and Chan, C.W. (2003). “Modelling of river discharges and rainfall using radial basis function networks based on support vector regression.” International J. of Systems Science, 34(14-15), 763-773.
13- Yu, X., Liong, S.-Y., and Babovic, V. (2004). “EC-SVM approach for realtime hydrologic forecasting.” J. of Hydroinformatics, 6, 209-223.
14- Bray, M., and Han, D. (2004). “Identification of support vector machines for runoff modeling.” J. of Hydroinformatics, 6 (4), 265-280.
15- Dibike, Y.B., Velickov, S., Solomatine, D., and Abbott, M.B. (2001). “Model induction with support vector machines: Introduction and applications.” J. of Computing in Civil Engineering, 15 (3), 208-216.
16- Tripathi, Sh., Srinivas, V. V., and Nanjundiah, R. S. (2006). “Downscaling of precipitation for climate change scenarios: A support vector machine approach.” J. of Hydrology, 330, 62-640.
17- Wang, W. C., and Men, W. L. (2008). “Online prediction model based on support vector machine.” Neurocomputing, 71, 550-558.
18- Behzad, M., Asghari, K., Eazi, M., and Palhang, M. (2009). “Generalization performance of support vector machines and neural networks runoff modeling.” Expert System with Applications, 36, 7624-7629.
19- Chen, S-T., Yu, P.Sh., and Tang, H. Y. (2010). “Statistical downscaling of daily precipitation using support vector machines and multivariate analysis.” J. of Hydrology, 385, 13-23.
20- Lin, G., Chen, G., Huang, P., and Chou, Y. (2009). “Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods.” J. of Hydrology, 372, 17-29.
21- Kisi, O., and Cimen, M. (2011). “A wavelet-support vector machine conjunction model for monthly streamflow forecasting.”
J. of Hydrology,
399(1-2), 132-140.
22- Nooria, R. A.R., Karbassia, A., Moghaddamniac, D., Hand, M.H., Zokaei-Ashtianie, A., Farokhniab, F., and Ghafari Goushehc, M. (2011). “Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction .” J. of Hydrology, 401 (3-4), 177-189.
23- McKee, T.B., Doesken, N.J., and Kleist, J. (1993). “The relationship of drought frequency and duration to time scales.” In: Proceedings of the Eighth Conference on Applied Climatology. Am. Meteor. Soc., Boston, 179-184.
24- Pai, P.-F., and Hong, W.-C. (2007). “A recurrent support vector regression model in rainfall forecasting,” Hydrological Processes, 21, 819-827.
25- Witten Ian H., and Eibe, F. (2005). Data mining: Practical machine learning tools and techniques, Morgan Kaufmann Pub., Amsterdam.
26- Peng, H.C., Long, F., and Ding, C. (2005). “Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 1226-1232.
27- Chang, C.-C., and Lin, C.-J. (2009). “LIBSVM: A library for support vector machines.” <http://www.csie.ntu.edu.tw/~cjlin/libsvm>.(Version 2.91, November 2009).