Abedi-Koupai J, Amiri MJ, Eslamian SS (2009) Comparison of artificial neural network and physically based models for estimating of reference evapotranspiration in greenhouse. Australian Journal of Basic and Applied Sciences 33:2528-2535
Allen RG (1999) Reference evapotranspiration calculation software for FAO and ASCE standardized equations. University of Idaho Research and Extension Center, 76p
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop water requirements. Irrigation and Drainage Paper 56 Rome: Food and Agriculture Organization of the United Nations, 300p
Cuenca RH (1989) Irrigation system design: An engineering approach Englewood Cliffs. NJ, Prentice-Hall, 552p
Dehbozorgi F, Sepaskhah AR (2012) Comparison of artificial neural networks and prediction models for reference evapotranspiration estimation in a semi-arid region. Archives of Agronomy and Soil Science 585:477–497 doi:10.1080/03650340.2010.530255
Ghasemi A, Zare Abyaneh H, Amiri Chaichian R, Mohammadi K (2007) Assessing artificial neural network and empirical methods to estimate the reference evapotranspiration of Hamedan province in Iran. Proceedings of the 9th Conference of Irrigation and decreasing the evapotranspiration. Kerman, Iran (In Persian).
Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Applied Engineer in Agriculture 12:96–99
Jain SK, Nayak PC, Sudheer KP (2008) Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation. Hydrological Processes 2213:2225–2234 doi:10.1002/hyp.6819
Jensen ME, Burman RD, Allen RG (1990) Evapotranspiration and irrigation water requirements. American Society of Civil Engineers, Engrg Pract Manual No. 70, 332p
Kisi O, Kilic Y (2015). An investigation on generalization ability of artificial neural networks and M5 model tree in modeling reference evapotranspiration. Theoretical and Applied Climatology. doi:10.1007/s00704-015-1582-z
Kumar M, Raghuwanshi N, Singh R, Wallender W, and Pruitt W (2002) Estimating evapotranspiration using artificial neural network. Journal of Irrigation and Drainage Engineering 1284:224-233 doi:10.1061/ASCE0733-94372002128:4224, 224-233
Landeras G, Ortiz-Barredo A, López JJ (2008) Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country Northern Spain. Agricultural Water Management 955:553–565 doi:10.1016/j.agwat. 2007.12.011
Razzaghi F, Sepaskhah AR (2010) Assessment of nine different equations for ETo estimation using lysimeter data in a semi-arid environment. Archives of Agronomy and Soil Science 561:1–12 doi:10.1080/03650340902829180
Shamshirband S, Amirmojahedi M, Gocić M, Akib S, Petković D, Piri J, Trajkovic S (2015) Estimation of reference evapotranspiration using neural networks and Cuckoo search algorithm. Journal of Irrigation and Drainage Engineering 142(2):04015044 doi:10.1061/(ASCE) IR.1943-4774.0000949, 04015044
Shiri J, Kişi Ö (2011) Application of artificial intelligence to estimate daily pan evaporation using available and estimated climatic data in the Khozestan province south western Iran. Journal of Irrigation and Drainage Engineering 1377:412-425 doi:10.1061/ASCEIR.1943-4774.0000315, 412-425
Shiri J, Marti P, Nazemi A H, Sadraddini A A, Kisi O, Landeras G, Fakheri Fard A (2015) Local vs. external training of neuro-fuzzy and neural networks models for estimating reference evapotranspiration assessed through k-fold testing. Hydrology Research 46 (1) 72-88
Trajkovic S (2005) Temperature-based approaches for estimating reference evapotranspiration. Journal of Irrigation and Drainage Engineering 1314:316–323 doi:10.1061/asce0733-94372005131:4316
Trajkovic S, Todorovic B, Stankovic M (2003) Forecasting of reference evapotranspiration by artificial neural networks. Journal of Irrigation and Drainage Engineering 129(6):454-457 doi:10.1061/ASCE0733-94372003129:6454, 454-457
Traore S, Luo Y, Fipps G (2016) Deployment of artificial neural network for short-term forecasting of evapotranspiration using public weather forecast restricted messages. Agricultural Water Management, 163:363–379
Traore S, Wang YM, Kerh T (2010) Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone. Agricultural Water Management 975:707–714 doi:10.1016/j.agwat.2010.01.002
Zare Abyaneh H, Gasemi A, Bayat Varkeshi M, Mohammadi K, Sabziparvar AA (2009) Evaluation of two artificial neural network software in the prediction of crop reference evapotranspiration. Water and Soil Science 19(1):201-212 (In Persian)