References
  -  G. Rana, N. Katerji, M. Mastrorilli, Environmental and soilplant
    parameters for modelling actual crop evapotranspiration
    under water stress conditions, Ecol. Model, 101 (1997) 363–371. 
 
  -  R.G. Allen, L.S. Pereira, D. Raes, M. Smith, Crop Evapotranspiration:
    Guidelines for computing crop water requirements. Rome,
    Italy, FAO Irrigation Drainage Paper, (1998) No. 56, 300p. 
 
  -  J. Doorenbos, W.O. Pruitt, Crop Water Requirements, Irrigation
    Drainage Paper, No. 24. FAO. (1977). Rome, Italy. 
 
  -  M. Kumar, N. Raghuwanshi, R. Singh, W. Wallender, W. Pruitt,
    Estimating evapotranspiration using artificial neural network,
    J. Irrig. Drain. E, 128(4) (2002) 224–233. 
 
  -  A.J. Cannon, P.H. Whitfield, Downscaling recent streamflow
    conditions in British Columbia, Canada using ensemble neural
    network models, J. Hydrol., 259 (2002) 136–151. 
 
  -  O. Kişi, Evapotranspiration estimation using feed-forward
    neural networks, J. Hydrol. Res., 37(3) (2006) 247–260. 
 
  -  S. Zanetti, E. Sousa, V. Oliveira, F. Almeida, S. Bernardo, Estimating
    evapotranspiration using artificial neural network and
    minimum climatological data, J. Irrig. Drain. E., 133(2) (2007)
    83–89. 
 
  -  A.J. Adeloye, R. Rustum, I.D. Kariyama, Neural computing
    modeling of the reference crop evapotranspiration, Environ.
    Model. Softw., 29(1) (2012) 61–73. 
 
  -  Ladlani, L. Houichi, L. Djemili, H. Salim, B. Khaled, Modeling
    daily reference evapotranspiration (ET0) in the north of Algeria
    using generalized regression neural networks (GRNN) and
    radial basis function neural networks (RBFNN): a comparative
    study, Meteorol. Atmos. Phys., 118 (2012) 163–178. 
 
  -  O.O. Aladenola, C.A. Madramootoo, Evaluation of solar radiation
    estimation methods for reference evapotranspiration
    estimation in Canada, Theor. Appl. Climatol., 118(3) (2014)
    377–385. 
 
  -  O. Kisi, M. Cimen, Evapotranspiration modelling using
    support vector machines, Hydrolog. Sci. J., 54(5) (2009)
    918–928. 
 
  -  X. Wen, J. Si , Z. He, J. Wu, H. Shao, H. Yu, Support-vector-machine-based models or modeling daily reference evapotranspiration
    with limited climatic data in extreme arid regions,
    Water Resour Manag., 29 (2015) 3195–3209. 
 
  -  O. Baydaroğlu, K. Koçak, K. Duran, River flow prediction using
    hybrid models of support vector regression with the wavelet
    transform, singular spectrum analysis and chaotic approach,
    Meteorol. Atmos. Phys., (2017). 
 
  -  S. Trajkovic, M. Stankovic, B. Todorovic, Estimation of FAO
    Blaney-Criddle b Factor by RBF Network, J. Irrig. Drain. E,
    ASCE, 126(4) (2000) 268–27. 
 
  -  J. Shiri, P. Marti, A.H. Nazemi, A.A. Sadraddini, O. Kisi, G.
    Landeras, A. Fakheri Fard, Local vs. external training of neuro-fuzzy and neural networks models for estimating reference
    evapotranspiration assessed through k-fold testing, J. Hydrol.
    Res., 46(1) (2015) 72–88. 
 
  -  P. Marti, P. González-Altozano, R. López-Urrea, L.A. Mancha,
    J. Shiri, Modeling reference evapotranspiration with calculated
    targets: Assessment and implications, Agric. Water
    Manag., 149 (2015) 81–90. 
 
  -  J. Shiri, A.A Sadraddini,A.H. Nazemi, P. Marti, A. Fakheri
    Fard, O. Kisi, G. Landeras, Independent testing for assessing
    the calibration of the Hargreaves–Samani equation: New heuristic
    alternatives for Iran, Comput. Electron. Agr., 117 (2015)
    70–80. 
 
  -  J. Shiri, A.H. Nazemi, A.A. Sadraddini, G. Landeras, O. Kisi, A.
    Fakheri Fard, P. Marti, Comparison of heuristic and empirical
    approaches for estimating reference evapotranspiration from
    limited inputs in Iran, Comput. Electron. Agr., 108 (2014) 230–
    241. 
 
  -  S. Kim, J. Shiri, V.P. Singh, O. Kisi, G. Landeras, Predicting
    daily pan evaporation by soft computing models with limited
    climatic data, Hydrolog. Sci. J., 60(6) (2015) 1120–1136. 
 
  -  G. Landeras, E. Bekoe, J. Ampofo, F. Logah, M. Diop, M. Cisse,
    J. Shiri, New alternatives for reference evapotranspiration estimation
    in West Africa using limited weather data and ancillary
    data supply strategies, Theor. Appl. Climatol., (2017) DOI:
    10.1007/s00704-017-2120-y. 
 
  -  J. Shiri, Evaluation of FAO56-PM, empirical, semi-empirical
    and gene expression programming approaches for estimating
    daily reference evapotranspiration in hyper-arid regions of
    Iran, Agric. Water Manag., 188 (2017) 101–114. 
 
  -  J. Shiri, P. Marti, V.P. Singh, Evaluation of gene expression
    programming approaches for estimating daily evaporation
    through spatial and temporal data scanning, Hydrol. Process.,
    28(3) (2014) 1215–1225. 
 
  -  R. Fletcher, (2013) Practical Methods of Optimization: John
    Wiley & Sons, Chichester, West Sussex England. 
 
  -  B. Guo, S.R. Gunn, R.I. Damper, J.D. Nelso, Customizing kernel
    functions for SVM-based hyperspectral image classification,
    IEEE Trans. Image Process, 17(4) (2008) 622–629. 
 
  -  T. Kavzoglu, I. Colkesen, A kernel functions analysis for support
    vector machines for land cover classification, Int. J. Appl.
    Earth Obs. Geoinf., 11(5) (2009) 352–359. 
 
  -  D. Basak, S. Pal, D.C. Patranabis, Support vector regression,
    Natl. Westm. Bank Q R., 11(10) (2007) 203–224. 
 
  -  G.Q. Liu, (2011) Comparison of regression and ARIMA models
    with neural network models to forecast the daily streamflow
    of White Clay Creek, Dissertation, University of Delaware. 
 
  -  V.N. Vapnik, A.J. Chervonenkis, The necessary and sufficient
    conditions for consistency of the method of empirical risk, Pattern
    Recog. Image Anal., 1(3) (1991) 284–305. 
 
  -  R.S Govindaraju, Artificial neural networks in hydrology, I:
    Preliminary concepts, J. Hydro. Eng., 5(2) (2000) 115–123. 
 
  -  C.M. Zealand, D.H. Burn, S.P. Simonovic, Short term streamflow
    forecasting using artificial neural networks, J. Hydrology,
	  214(1) (1999) 32–48.