Aghashariatmadary, Z. (2011). Evaluation of model for estimating total solar radiation at horizontal surfaces based on meteorological data, with emphasis on the performance of the angstrom model over iran. Ph. D. dissertation, University of Tehran. College of Agriculture and Natural Resources. (In Farsi).
Angstrom, A. 1924. Solar and terrestrial radiation. Quart .J. Roy. Met, 50: 121-125.
Ball RA, Purcell LC, Carey SK (2004) Evaluation of solar radiation prediction models in North America. Agronomy Journal 96:391–397
Azadeh, A., Maghsoudi, A. and Sohrabkhani, S. 2009. An integrated artificial neural networks approach for predicting global radiation. Energy Conversion and Management, 50: 1497–1505.
Azeez MAA (2011) Artificial neural network estimation of global solar radiation using meteorological parameters in Gusau, Nigeria. Artificial Applied Science Research 3(2):586–95
Biazar, S.M. (2017) Input variables determination using Gamma test and Entropy theory for daily evaporation prediction. Thesis is approved for the degree of Master of Science in Water Resources. Department of Water Engineering, Faculty of Agriculture, University of Tabriz. September 2017. (In Farsi)
Chen JL, Li GS, Wu SJ (2013) Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration. Energy Conversion and Management 75:311–318
Dawson, C.W., Abrahart, R.J., Shamseldin, A.Y. and R.L. Wibly. 2006, Flood estimation at ungauged sites using artificial neural networks. Journal of Hydrology. 319 (1-4): 391-409.
Ferreira, C. 2001. Gene expression programming: a new adaptive algorithm for solvingproblems. Complex Systems, Vol.13(2): 87–129.
Hargreaves, G.H., and Samani, Z.A. 1982. Estimating potential evapotranspiration. Journal of Irrigation and Drainage Engineering ASCE, 108: 223-230.
Harmancioglu, N. B. 1984. Entropy concept as used in determination of optimum sampling intervals. Proc. of Hydrosoft 84, International Conf. on Hydraulic Engineering Software, September 10-14, 1984. Portoroz, Yugoslavia, pp. 6-99 and 6-110.
Helena, B., Pardo, R., Vega, M., Barrado, E., Fernandez, J.M. and L. Fernandez. 2000. Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis. Water Research 34(3): 807–816.
Jahani, B., and Mohammadi, B. 2018. A comparison between the application of empirical and ANN methods for estimation of daily global solar radiation in Iran. Theor Appl Climatol (2018). https://doi.org/10.1007/s00704-018-2666-3
Kamali, S., Aghasariatmadari, Z. (2018). Evaluation of the effects of atmospheric pollutants on the performance of Angestrom- Prescott equation in estimating solar radiation (Case Study: Karaj). Iranian Journal of Soil and Water Research, 48(5), 1053-1061. doi: 10.22059/ ijswr. 2018. 233499. 667682
Khalili and Rezai Sadr. (1997). Estimation of solar radiation in iran, based on climate data. Journal of Geographical Research 84: 15-35. (In Farsi).
Lazzus JA, Ponce AAP, Marin J (2011) Estimation of global solar radiation over the city of La Serena (Chile) using a neural network. Applied Solar Energy 47(1):66–73
Long H, Zhang Z, Su Y (2014) Analysis of daily solar power prediction with data-driven approaches. Applied Energy 126:29–37
Mohammadi, B. (2017) Daily Evaporation prediction based on a hybridization of Artificial Neural Network and firefly Optimization Algorithm. Thesis is approved for the degree of Master of Science in Water Resources. Department of Water Engineering, Faculty of Agriculture, University of Tabriz. July 2017. (In Farsi)
Mohammadi, B. Emamgholizadeh, S. (2017) Using principal component analysis to inputs the effective rainfall estimates based on entries to help support vector machine and artificial neural network. Journal of Rainwater Catchment Systems. 2017; 4 (4) ,67-75. (In Farsi)
Mohammadi, B. Ghorbani, M A. (2016) Gamma test application in input preprocessing for time series modeling of rainfall. Journal of Rainwater Catchment Systems. 2016; 4 (3) ,61-72. (In Farsi)
Mohammadi B, Moazenzadeh R. (2017) Uncertainty analysis of artificial neural network models and support vector machine in rainfall estimation. Journal of Rainwater Catchment Systems. 2017; 5 (1) :43-50. (In Farsi)
Noori, R., Karbassi, A. and M. Sabahi. 2010. Evaluation of PCA and gamma testtechniques on ANN operation for weekly solid waste prediction. Journal of Environmental Management. 91(3): 767-771. 20. Singh, VP. and CY. Xu. 1997. Evaluation and generalization of 13 mass transfer equations for determining free water evaporation. Hydrological Process. 11:311–324
Pai, P.F. and Hong, W.C., 2007. A recurrent support vector regression model in rainfall forecasting. Hydrological Processes: An International Journal, 21(6), pp.819-827
Rahimikhoob A. 2010. Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment. Renew. Energy. 35, 2131-2135.
Remesan, R. Shamim, M.A. and Han, D. (2008). Model data selection using gamma test for daily solar radiation estimation. Hydrological Processes, 22, 4301-4309.
Redy, K.S., and Ranjan, M. 2003. Solar resource estimation using artificial neural networks and comparison with other correlation models. J. Energy Conversion and Management, 44: 2519-2530.
Sabziparvar A.A., and Shetaee H. 2007. Estimation of global solar radiation in arid and semi-arid climates of East and West Iran, Energy 32: 649–655.
Shamshirband S, Mohammadi K, Chen HL, Narayana Samy G, Petković D, Ma C.(2015) Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: A case study for Iran. J Atmos Solar-Terrestrial Phys. 2015;134:109-117. doi:10.1016/j.jastp.2015.09.014
Soltani, S., and Morid, M. (2005). Comparative estimation of global solar radiation using Hargeavessamani and artificial neural network methodologies, Journal of Science of Agriculture, 15: 69-77. (In Farsi)
Skeiker K. 2006. Correlation of global solar radiation with common geographical and meteorological parameters for Damascus province, Syria, Energy Conversion and Management, Mgmt, 47: 331-345.
Tymvios, F. S., Jacovides, C. P., Michaelides, S. C., and Scouteli, C. 2005. Comparative study of Angstrom and artificial neural network methodologies in estimating global solar radiation. J. Solar Energy, 78: 752-762.
Xu, H. Xu, C. Y, Sælthun, N. R. Xu, Y. Zhou, B and Chen, H. 2015. Entropy theory based multi-criteria resampling of rain gauge networks for hydrological modelling – A case study of humid area in southern China. Journal of Hydrology. 525, 138-151.