Agarwal, K.N., Verma V.V. 1978. Sun: Mankind’s future source of energy proceedings of the international solar. In: Energy society congress. 1, 415–429.
Alizadeh, A., Khalili, N. 2009. Estimation of Angstrom Coefficient and Developing a Regression Equation of Solar Radiation Estimation (Case study: Mashhad). journal of Water and Soil. 23(1), 229-238. (In Farsi)
Allen, R.G., Pereira, L. S., Raes, D., Smith, M. 1998. Crop evapotranspiration-guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56. United Nations Food and Agriculture Organization, Rome.
Almorox, J., C. Hontoria, 2004. Global solar radiation estimation using sunshine duration in Spain. Energy Conversion and Management, 45(9–10), 1529–1535.
Ananthakrishnan S., Prasad, R., Stallard, D., Natarajan, P. 2013. Batch-mode semi-supervised active learning for statistical machine translation. Computer Speech and Language. 27, 397–406.
Angstrom, A. K., 1924. Solar and terrestrial radiation. Quarterly Journal of Royal Meteorological Society, 50, 121-125.
Avazpour, S., Bakhtiari, B., Qaderi, K. 2019. Evaluation of artificial neural network and support vector machine methods in estimating total solar radiation at Kerman and Yazd. Journal of Renewable and New Energy, 7(2), 19-38.
Ayana, H. B. 2011. Solar energy resources as an important part of renewable energy resources in terms of Turkey. Project for the Degree of MSc in Energy. London: Environmental Technology and Economics, City University.
Azeez, M. A. A. 2011. Artificial neural network estimation of global solar radiation using meteorological parameters in Gusau, Nigeria. Artificial Applied Science Research, 3(2), 586–95.
Babiker IS, Mohamed MA, Hiyama T, Kato K. 2005. A GIS-based DRASTIC model for assessing aquifer vulnerability in Kakamigahara Heights, Gifu Prefecture, central Japan. Science of the Total Environment. 345(1), 127-40.
Badescu, V. 2008. Modeling soalr radiation at the earth surface. Verlag Berlin Heidelberg. Springer
Ball, R.A., Purcell, L.C., and Carey, S.K. 2004. Evaluation of solar radiation prediction models in North America. Agronomy Journal, 96, 391–397.
Belcher, B.N. and DeGaetano, A. T. 2007. A revised empirical model to estimate solar radiation using automated surface weather observations. Solar Energy, 81(3), 329–345.
Bristow, K. L. and Campbell, G. S. 1984. On the relationship between incoming solar radiation and daily maximum and mnimum temperature, Agricultural and Forest Meteorology, 31, 159-166.
Chen, JL, Liu, H.B., Wu, W., Xie, D.T. 2011. Estimation of monthly solar radiation from measured temperatures using support vector machines‒ A case study. Renewable Energy, 36, 413‒ 420.
De Souza, J. L., Nicacio, R. L., Lima Moura, M. A. 2005. Global solar radiation measurements in Maceio Brazil. Agricultural water Management, 30, 1203 – 1220.
Ebrahimpour, A., Marefat, M., Naeri, H. 2009. Presenting a new relationship for estimating total radiation in different climates of Iran. Jornal of Geographical Space, 9(25), 1-22 (In farsi)
Ekici, B.B. 2014. A least squares support vector machine model for prediction of the next day solar insolation for effective use of PV systems. Measurement, 50, 255‒262.
Feng, Y., Zhang, X., Jia, Y., Cui, N., Hao, W., Li, H., Gong, D. 2021. High-resolution assessment of solar radiation and energy potential in China. Energy Conversion and Management. 240, 114265
Glover, J., Colocj, M. 1957. The Empirical relation between solar radiation and hours of sunshine. Quarterly journal of the Royal Meteorological Society, 84(360), 172-175.
Hargreaves, G. H., Samani, Z. A. 1982. Estimating potential evapotranspiration. journal of Irrigation and Drainage Engineering, 108, 225-230.
He, C., Liu, J., Xu, F., Zhang, T., Chen, S., Sun, Z., Zheng, W., Wang, R., He, L., Feng, H., Yu, Q., He, J. 2020. Improving solar radiation estimation in China based on regional optimal combination of meteorological factors with machine learning methods. Energy Conversion and Management, 220 (113111), 1-15.
Hissou, H., Benkirane, S., Guezzaz, A., Azrour, M. and Beni-Hssane, A. 2023. A Novel Machine Learning Approach for Solar Radiation Estimation. Sustainability, 15 (10609), 1-21.
Iziomon, M.G. and Mayer, H. 2002. Assessment of some global solar radiation parameterizations.
Journal of Atmospheric and Solar-Terrestrial Physics, 64 (2), 1631-1643.
Jahantigh, N and Piri, J. 2022. Estimating Solar Radiation in Different Climates of Iran using Hybrid Machine Learning Methods. Journal of Applied and Computational Sciences in Mechanics, 35, 37-54. (In Farsi)
Jia, D., Yang, L., Lv, T., Liu, W., Gao, X. and Zhou, J. 2022. Evaluation of machine learning models for predicting daily global and diffuse solar radiation under different weather/pollution conditions. Renewable Energy, 187, 896-906.
Kamali, S. and Aghashariatmadary, Z. 2017. Investigating the effect of atmospheric pollutants on the efficiency of Angstrom-Prescott relation in estimating solar radiation (A case study of Karaj (. Iranian Journal of Soil and Water Research, 45 (5), 1051-1061. (In Farsi)
Khalili, A., Bazrafshan, J., Cheraghalizadeh, M. 2022. A Comparative study on climate maps of Iran in extended de Martonne classification and application of the method for world climate zoning. Journal of Agricultural Meteorology. 10(1), 3-16. (In Farsi).
Khalili, S. and Rezai Sadr, H. (1997). Estimation of solar radiation in iran, based on climate data. Journal of Geographical Research. 84, 15-35. (In Farsi).
Lazzus, J.A., Ponce, A.A.P. and 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.
Li M.F., Tang X.P., Wu W and Liu H.B, 2013. General models for estimating daily global solar radiation for different solar radiation zones in mainland China. Energy Conversion and Management. 70, 139-148.
Long, H., Zhang, Z. and Su, Y. 2014. Analysis of daily solar power prediction with data-driven approaches. Applied Energy. 126, 29–37.
Mehdizadeh S. and Behmanesh, J. 2016. Calibration of Angstrom-Prescott Equation Coefficients in the Selected Stations of Urmia Lake Watershed. Journal of Irrigation and Water engineering. 6(23), 78-91. (In Farsi)
Motamed Shariati, H., Mobli, H., Sharifi, M. and Ahmadi, H. 2016. Estimating Solar Radiation with Ordinary Meteorological Data in Mashhad. Iranian Journal of Biosystem Engineering. 47(1), 185-196. (In Farsi).
Nosrati, N., Masoompour Samakosh, J., Zolfaghari, h. and Ghahraman, A. 2021. Relationship between Atmospheric Water Vapor Transfer and Daily Rainfall in Iran. Arid Region Geographic Studies. 45(12), 1-13. (In Farsi)
Ornella L. and Tapia, E. 2010. Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data, Computers and Electronics in Agriculture. 74, 250–257.
Penman, H. L. 1956. Evaporation an Introductory Survey. Netherlands Journal of Agricultural Science. (4), 9-29.
Piri, j., Shamshirband, sh., Petkovic, D. and Wen Tong, C. 2014. Prediction of the solar radiation on the Earth using support vector regression technique. Infrared Physics & Technology. 68, 179-185.
Prescott, J. A., 1940, Evaporation from a Water Surface in Relation to Solar Radiation. Trans. R. Soc South Aust. 64, 114–118.
Ramedani, Z., Omid, M. and Keyhani, A. 2013. Modeling Solar Energy Potential in a Tehran Province Using Artificial Neural Network. International Journal of Green Energy. 10, 427-441.
Reddy, T. A., Gordon, J. M. and De Silva, I. P. 1987. MIRA, A one-repetitive day method for predicting the long-term performance of solar energy systems. Solar Energy, (Pergamon Press), 39(2), 123-133.
Rehman S, Mohandes M. 2009. Estimation of diffuse fraction of global solar radiation using artificial neural networks. Energy Sources, Part A. 31, 974–84.
Rehman, S. 1998. Solar radiation over Saudi Arabia and comparisons with empirical models. Energy. 23(12), 1077–1082
Rosen, L. 1994. A study of the DRASTIC methodology with emphasis on Swedish conditions. Ground Water. 32(2), 278-285.
Sabbagh, J., Sayigh, A. and Al-Salam, E. 1977. Estimation of the Total Solar Radiation from Meteorological Data. Solar Energy. 19, 307-311.
Seyedian, S.M., Farsasati, M., Rouhani, H. and Heshmatpor, A. 2017. Solar Radiation Prediction Using Metrological Parameters. Iran-Water Resources Research. 13(1), 88-100. (In Farsi).
Sharifi, S.S., Rezaverdinejad, V., Nourani, V. and Behmanesh, J. 2020. Evaluation of the Capability of Intelligent Models in Estimating Monthly Global Solar Radiation. Water and Soil Science. 31(2), 13-26. (In Farsi).
Take, a., Hasak Yildirim, H and Celik, O. 2015. Evaluation and performance comparison of different models for the estimation of solar radiation. Renewable and Sustainable Energy Reviews. 50, 1097-1107.
Tamer, K., Azah, M. and Sopian, K. 2012. A review of solar energy modelling techniques. Renewable Sustainable Energy Reviews. 16(5), 2864–2869.
Trnka, M., Zalud, Z., Eitzinger, J and Dubrovsky, M. 2005. Global solar radiation in Central European lowlands estimated by various empirical formulae. Agricultural and Forest Meteorology. 131 (1–2), 54–76.
Vapnik, V.N. 1998. The Support Vector Method of Function Estimation. Nonlinear Modeling. 55-85.
Yin, Y., S. Wu, D. Zheng and Yang, Q, 2008. Radiation calibration of FAO56 Penman–Monteith model to estimate reference crop evapotranspiration in China. Agricultural Water Management. 95, 77-84.
Zeng, J, and Qiao, W. 2013. Short-term solar power prediction using a support vector machine. Renewable Energy. 52, 118‒27.
Zhao, N., Zeng, X. and Han, Sh. 2013. Solar radiationestimation using sunshine hour and air pollution index in China. Energy conversion and Management. 846-851.