1- Wilhite, D.A., and Glantz,M.H. (1985).“Understanding the drought phenomenon: The role of definitions.” Water International, 10, 111-120.
2- Zahraie, B., Karamouz, M., and Eghdami, S. (2004). “Seasonal precipitation forecasting using large scale climate signals: Application to the Karoon river basin in Iran.” Proc., of the 6th International Conference on Hydroinformatics–Liong, Phoon and Babovic (eds),Singapore.
3- Bonaccorso, B., Bordi, I., Cancelliere, A., Rossi, G., and Sutera A. P. (2003). “Spatial variability of drought: An analysis of the SPI in sicily.” Water Resour. Manage., 17, 273-296.
4- Steinemann, A.(2003).“Drought indicators and triggers: A stochastic approach to evaluation.” J. of American Water Resources Association, 39(5), 1217-1233.
5- Loukas, A., and Vasiliades, L. (2004). “Probabilistic analysis of drought spatiotemporal characteristics in Thessaly region, Greece.” J. Natural Hazards and Earth System Sciences, 4, 719-731.
6- Labedzki, L., and Bak, B. (2005). “Drought mapping in polandusing SPI.” ICID Probabilistic Analysis of Drought Spatiotemporal Characteristics in Thessaly 21st European Regional Conference,Thessaly,Greece, 10-20.
7- Moreira, E.E., Paulo, A.A., Pereira, L.S., and Mexia, J.T. (2006). “Analysis of SPI drought class transitions using loglinear models.” J. Hydrology, 331, 349-359.
8- Mishra, A.K., and Desai, V.R. (2006). “Drought forecasting using feed-forward recursive neural network.” Ecol. Modell., 198, 127-138.
9- Paulo, A.A., and Pereira, L.S. (2007). “Prediction of SPI drought class transitions using Markov chains.” J. Water Resour. Manage., 21, 1813-1827.
10- Moreira, E.E., Coelho, C.A., Paulo, A.A., Pereira, L.S., and Mexia, J.T. (2008). “SPI-based drought category prediction using loglinear models.” J. Hydrology, 354, 116-130.
11- Mehmet, A. Y., and Mahmut, F. (2009). “Adaptive neuro fuzzy inference system approach for municipal water consumption modeling: An application to Izmir, Turkey.” J. Hydrology, 365, 225-234,
12- Aurélio, A., and
Carlos Roberto, D. (2008). “Application of fuzzy logic to the evaluation of runoff in a tropical watershed.”
Environmental Modelling and Software, 23, 244-253.
13- Nourani, V., and Salehi, K. (2008). “Rainfall-runoff modeling using ANFIS and ANN-wavelet models.” 4th National Civil Eng. Conf., University of Tehran,Tehran. (CD ROM), (In Persian)
15- Postizadeh, N. (2006). “River flow forecasting using fuzzy inference system.” MSc. Thesis of Hydraulic Structures, Dept. of Agriculture,TarbiatModarrsUniversity,Tehran. (In Persian)
16- Karamouz, M., Tabesh, M., Nazif, S., and Moridi, A. (2005). “Estimation of hydraulic pressure in water network using artificial neural network and fuzzy logic.” J. of Water and Wastewater, 56, 3-14. (In Persian)
17- Jamali, S., Abrishamchi, A., and Tajrishi, M. (2007). “River stream-flow and Zayanderoud reservoir operation modeling using the fuzzy inference system.” J. of Water and Wastewater, 64, 25-34. (In Persian)
18- Abedini, M. J., and Nasseri, M. (2008). “Inverse distance weighted revisited.” 4th APHW, Conf. Beijing, China, (CD ROM).
19-Lashkari, H. (1996). “Synoptic precipitation pattern of extreme south westIran.” Ph.D. Thesis of Climatology,TarbiatModarresUniversity,Tehran. (In Persian)
20- Bowden, G. J., Dandy G. C., and Maier, H. R. (2005a). “Input determination for neural network models in water resources applications, Part 1-background and methodology.” J. Hydrology, 301, 75-92.
21- Bowden, G. J., Dandy, G. C., and Maier, H. R. (2005b). “Input determination for neural network models in water resources applications, Part 2. Case study: Forecasting salinity in a river.” J. Hydrology., 301, 93-107
22- Nasseri, M., Asghari, K., and Abedini, M. J. (2008). “Optimized scenario of rainfall forecasting using genetic algorithms and artifitial neural networks.” Expert Systems with Applications., 35(3), 1415-1421.
23- Sudheer, K. P., Gosain, A. K., and Ramasastri, K. S. (2002). “A data driven algorithm for constructing artificial neural network rainfall-runoff models.” Hydrological Process, 16, 1325-1330.
24- Witten, I.H., and Frank, E. (2005). Data mining: Practical machine learning tools and techniques, Morgan Kaufmann Pub.,Amsterdam.
25- He, Z., Xu, X., and Deng, Sh. (2008). “k-ANMI: A mutual information based clustering algorithm for categorical data.” Information Fusion., 9, 223-233.
26- Maya, R. J., Dandy, G., Maier, H. R., and Nixon J. B. (2008). “Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems.” Environmental Modelling and Software., 23, 1289-1299.
27- Maya, R. J., Maier H. R., Dandy, G., and Fernando, G.T.M.K. (2008). “Non-linear variable selection for artificial neural networks using partial mutual information.” Environmental Modelling and Software., 23, 1312-1326.
28- Wua, J., Chen, J., Xiong, H., and Xie, M. (2008). “External validation measures for K-means clustering: A data distribution perspective.” Expert Systems with Applications, 36 (2), 6050-6061.