- Allen, R.G., Pereira, L.S., Raes, D. and Smith, M., 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), p.D05109.
- Almorox, J., Quej, V.H. and Martí, P., 2015. Global performance ranking of temperature-based approaches for evapotranspiration estimation considering Köppen climate classes. Journal of Hydrology, 528, pp.514-522.
- Berti, A., Tardivo, G., Chiaudani, A., Rech, F. and Borin, M., 2014. Assessing reference evapotranspiration by the Hargreaves method in north-eastern Italy. Agricultural Water Management, 140, pp.20-25.
- Blaney, H.F. and Criddle W.D., 1950. Determining Water Requirements in Irrigated Areas from Climatological and Irrigation Data; Soil Conservation Service Technical Paper 96; Soil Conservation Service, US Department of Agriculture:Washington, DC, USA.
- Djaman KB, Balde A, Sow A, Muller B, Irmak SK, N’Diaye M, Manneh BD, Moukoumbi Y, Futakuchi K, Saito K. Evaluation of sixteen reference Evapotranspiration methods under sahelian conditions in the Senegal River Valley. Journal of Hydrology Regional Study, 2015; 3: 139-159.
- Fallah, F. and Tahmasebipour, N. 2016. Meteorological drought analysis of Aligoudarz synoptic station using DIP software. 11th National Seminar on Watershed Management In date 2016-04-19 by Yasouj University,Watershed Management Society of Iran
- Ferreira, C., 2001. "Gene Expression Programming: A New Adaptive Algorithm for Solving Problems" Complex Systems, 13 (2), 87-129.
- Feyzollahpour, F., Delavar, M. and Hesamiafshar, M. 2017. Evaluation and Analysis of Uncertainty Estimation of Reference Evapotranspiration Using Reference Genetics. Journal of Soil and Water Science, Volume 27, Number 4, 147-135. [Persian]
- Gholami, V., Derakhshan, Sh. and Darwari, Z. 2012. Investigation of Multivariate Regression and Artificial Neural Network in Simulation of Groundwater Salinity in Mazandaran Province. Journal of Water Research in Agriculture, Volume 26, Number 3, pages 79-100. [Persian]
- Grismer, M.E., Orang, M., Snyder, R. and Matyac, R., 2002. Pan evaporation to reference evapotranspiration conversion methods. Journal of irrigation and drainage engineering, 128(3), pp.180-184.
- Hargreaves, G. H., 1994. Defining and using reference evapotranspiration. Journal of Irrigation and Drainage Engineering, 120(6), 1132-1139..
- Hojbar, H., Moazed, H. and Shokrikoochak, S. 2017. Simulation of Reference Evapotranspiration using empirical methods and artificial neural network and compare with leisimetere data in Urmia Kherizi station. Journal of Irrigation and Water Engineering, 4(15), 13-25. [Persian]
- Hosseini, S.M.R., Ganjikhoramdel, N. and Khalatabadifarahani, A.H. 2015. Evaluation and sensitivity analysis of different methods of estimating daily reference evapotranspiration in a cold climate. Journal of Applied Research in Water Sciences, Vol. 1, No. 2, Winter 2015, pp. 29-40. [Persian]
- Khoshhal, J., Zareabyane, H., Joshani, A.R. and Khazaee, M. 2016. Evaluation of different potential evapotranspiration methods with pan-FAO model in East and Northeast of Iran. Journal of Natural Geography, 8(28), 1-16. [Persian]
- Kisi, O. and Alizamir, M., 2018. Modelling reference evapotranspiration using a new wavelet conjunction heuristic method: Wavelet extreme learning machine vs wavelet neural networks. Agricultural and forest meteorology, 263, pp.41-48.
- Laaboudi, A., Mouhouche, B. and Draoui, B., 2012. Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions. International journal of biometeorology, 56(5), pp.831-841.
- Ladlani, I., Hauichi, L., Dhemili, L., Heddem, S. and Blouze, KH., Estimation of daily refrence evapotranspiration in the north of Algeria using adaptive neuro-Fuzzy inference system (ANFIS) and multiple linear regression Models: a comparative study. Arabian Journal for Science and Engineering, 2014; 39: 5959-5969.DOI 1007/s13369-014-1151-2
- Liu, X., Xu, C., Zhong, X., Li, Y., Yuan, X. and Cao, J., 2017. Comparison of 16 models for reference crop evapotranspiration against weighing lysimeter measurement. Agricultural water management, 184, pp.145-155.
- Lopes, H.S. and Weinert, W.R., 2004. EGIPSYS: an enhanced gene expression programming approach for symbolic regression problems. International Journal of Applied Mathematics and Computer Science, 14(3), pp.375-384.
- Makkink, G.F., 1957. Testing the Penman formula by means of lysimeters. J. Instit. Water Eng., 11, 277–288.
- Martí, P., González-Altozano, P., López-Urrea, R., Mancha, L.A. and Shiri, J., 2015. Modeling reference evapotranspiration with calculated targets. Assessment and implications. Agricultural Water Management, 149, pp.81-90.
- Niaghi, A.R., Majnooni-Heris, A., Haghi, D.Z. and Mahtabi, G., 2013. Evaluate several potential evapotranspiration methods for regional use in Tabriz, Iran. Journal of Applied Environmental and Biological Sciences, 3(6), pp.31-41.
- Pour-Ali Baba, A., Shiri, J., Kisi, O., Fard, A.F., Kim, S. and Amini, R., 2013. Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Hydrology Research, 44(1), pp.131-146.
- Raouf, M. and Azizimobser, J. 2017. Evaluation of eighteen Reference Evapotranspiration model in Ardabil climatic conditions. Water and Soil Conservation Journal, Volume 24, Number6. [Persian]
- Sarmadian, F., Mehrjerdi, R., Asgari, H. and Akbarzadeh, A. 2010. Comparison of Neuro-Fuzzy Neural Network and Multivariate Regression in Predicting Some Soil Properties, Journal of Watershed Research, Volume 41, Number 1, pages 211 to 220. [Persian]
- Sattari, M.H. and Esmailzadeh, B. 2016. Comparison of the results of M5 tree model and genetic programming with FAO-Penman-Monteith method for reference evapotranspiration reference. Journal of Water Resources Engineering, Ninth Year, Winter 2016. [Persian]
- Shiri, J., Nazemi, A.H., Sadraddini, A.A., Landeras, G., Kisi, O., Fard, A.F. and Marti, P., 2014. Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran. Computers and Electronics in Agriculture, 108, pp.230-241.
- Shiri, J., 2017. Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran. Agricultural water management, 188, pp.101-114.
- Tabari, H., Grismer, M.E. and Trajkovic, S. 2013. Comparative analysis of 31 reference Evapotranspiration methods under humid conditions. Irrigation Science, 31: 107-117.
- Tabari, H., Kisi, O., Ezani, A. and Talaee, PH. 2012. SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. Journal of Hydrology, 444, 78-89.
- Turc, L., 1961. Estimation of irrigation water requirements, potential evapotranspiration: A simple climatic formula evolved up to date. Ann. Agron., 12, 13–49.
- Wen, X., Si, J., He, Z., Wu, J., Shao, H. and Yu, H. 2015. Support-vector-machine-based models for modeling daily reference evapotranspiration with limited climatic data in extreme arid regions. Water resources management, 29(9), 3195-3209.
- Yassin, M.A., Alazba, A.A. and Mattar, MA. 2016. Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate. Agricultural Water Management, 163, 110-124.
|
