1- Alizadeh A., Erfanian M., Ansari H.2011. Assessment of Teleconnection Patterns Affecting Rainfall and Temperature (Case Study: Mashhad Synoptic Station), Iranian Journal of lrrigation and drainage, 2(5): 176-185.) In Persian with English abstract(.
2- Almeira G.J., Scian B.2006. Some atmospheric and oceanic indices as predictors of seasonal rainfall in the Del Plata Basin of Argentina. Journal of Hydrology, 329: 350–359.
3- Drosdowsky W., Chambers L.E. 2001. Near global scale sea surface temperature anomalies as predictors of Australian seasonal rainfall, Journal of climate, 14:1677–1687.
4- Fallah-Ghalhari G.A. 2014. Rainfall Prediction Using Teleconnection Patterns Through the Application of Artificial Neural Networks, Modern Climatology. Book 1. Dr Shih-Yu Wang (Ed.), 361-386.
5- Gomez V., Casanovas A. 2002. Fuzzy logic and meteorological variables: a case study of solar irradiance, Fuzzy Sets and Systems, 126:121–128.
6- Jacovides C.P. 1998. Reply to comment on 'Statistical procedures for the evaluation of evapotranspiration computing models'. Agricultural Water Management, 37: 95-97.
7- Jang J.S.R. 1993. ANFIS: Adaptive-network-based fuzzy inference system, IEEE Trans. Syst., Man and Cybernetics, 23(3): 665–684.
8- Keskin M.E., Terzi O., Tayalan D. 2004. Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey. Hydrological Sciences–Journal–des Sciences Hydrologiques, 49(6):1001-1010.
9- Kim T.W., Valde´s J.B., Nijssen B., Roncayolo D. 2006. Quantification of linkages between large-scale climatic patterns and precipitation in the Colorado River Basin. Journal of Hydrology, 321:173–186.
10- Mekanik F., and Imteaz M.A. 2012. A Multivariate Artificial Neural Network Approach for Rainfall Forecasting: Case Study of Victoria, Australia. p. 1-5. Proceedings of the World Congress on Engineering and Computer Science, Vol I, 24-26 October 2012. WCECS, San Francisco, USA
11- Nazemosadat M.J., Setoodeh P., Safavi A. 2008. Improving Neural Network Models for forecasting Seasonal Precipitation in Southwestern Iran: The Evaluation of Oceanic- Atmospheric indices. Asia Oceania Geosciences Society 5th Annual General Meeting, at Busan Exhibition & Convention Center, Busan, South Korea.
12- Pongracz R., Bartholy J., and Bogardi I. 2001.Fuzzy rule-based prediction of monthly precipitation, Journal of Physical Chemistry B, 26(9):663–667.
13- Rezaee-Banafshe M., Jahan-Bakhsh S., Bayati-Khatibi M., Zeynali B. 2011. Forecast of Autumn and Winter Precipitation of West Iran by Use from Summer and Autumn Mediterranean Sea Surface Temperature, Physical Geography Research Quarterly 74, 47-62. (in Persian with English abstract).
14- Rezaeian-Zadeh M., Tabari H. 2012. MLP-based drought forecasting in different climatic regions. Theoretical and Applied Climatology, 109(3-4): 407-414.
15- Sabbagh J., Sayigh A.A.M., Al-Salam E.M.A. 1977. Estimation of the total solar radiation from meteorological data. Solar Energy, 19(3): 307-311.
16- Sedaghat Kerdar A., Fatahi E. 2008. Drought Early Warning Methods over Iran, Geography and Development Iranian Journal, 11: 76-89. (in Persian with English abstract).
17- Shukla R.P., Tripathi K.C., Pandey A.C., Das I.M.L. 2011. Prediction of Indian summer monsoon rainfall using Niño indices: A neural network approach, Atmospheric Research, 102 (1–2): 99–109.
18- Smith, T.M., Arkin P.A., Sapiano M.R.P., CHang C.Y. 2010. Merged Statistical Analyses of Historical Monthly Precipitation Anomalies Beginning 1900, Journal of Climate, 23: 5755-5770.