References

  1. Y. Feng, Y. Peng, N. Cui, D. Gong, K. Zhang, Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data, Comput. Electron. Agric., 136 (2017) 71–78.
  2. H. Sanikhani, O. Kisi, M.R. Nikpour, Y. Dinpashoh, Estimation of daily pan evaporation using two different adaptive neurofuzzy computing techniques, Water Resour. Manage., 26 (2012) 4347–4365.
  3. B. Mohammadi, S. Mehdizadeh, Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm, Agric. Water Manage., 237 (2020) 106145, doi: 10.1016/j. agwat.2020.106145.
  4. N.K. Tyagi, D.K. Sharma, S.K. Luthra, Determination of evapotranspiration and crop coefficients of rice and sunflower with lysimeter, Agric. Water Manage., 45 (2000) 41–54.
  5. F.J. Chang, L.C. Chang, H.S. Kao, G.R. Wu, Assessing the effort of meteorological variables for evaporation estimation by self-organizing map neural network, J. Hydrol., 384 (2010) 118–129.
  6. D. Skarlatos, I.K. Kalavrouziotis, C.R. Montes, A.J. Melfi, B.F.F. Pereira, Wastewater reuse in citrus: a fuzzy logic model for optimum evapotranspiration, Desal. Water Treat., 55 (2015) 315–324.
  7. N. Ücler, F. Kutlu, Estimating daily pan evaporation data using adaptive neuro fuzzy inference system: case study within Van Local Station-Turkey, J. Polytech., 900 (2020) 195–204.
  8. J. Shiri, W. Dierickx, A. Pour-Ali Baba, S. Neamati, M.A. Ghorbani, Estimating daily pan evaporation from climatic data of the State of Illinois, USA using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN), Hydrol. Res., 42 (2011) 491–502.
  9. O. Kisi, Applicability of Mamdani and Sugeno fuzzy genetic approaches for modeling reference evapotranspiration, J. Hydrol., 504 (2013) 160–170.
  10. M.K. Goyal, B. Bharti, J. Quilty, J. Adamowski, A. Pandey, Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS, Expert Syst. Appl., 41 (2014) 5267–5276.
  11. Y. Feng, N. Cui, D. Gong, Q. Zhang, L. Zhao, Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling, Agric. Water Manage., 193 (2017) 163–173.
  12. A.K. Mousa, M.S. Croock, M.N. Abdullah, Fuzzy based decision support model for irrigation system management, Int. J. Comput. Appl., 104 (2014) 14–20.
  13. M.L. Roderick, L.D. Rotstayn, G.D. Farquhar, M.T. Hobbins, On the attribution of changing pan evaporation, Geophys. Res. Lett., 34 (2007) 1–6.
  14. A. Pandey, R. Prasad, V.P. Singh, S.K. Jha, K.K. Shukla, Crop parameters estimation by fuzzy inference system using X-band scatterometer data, Adv. Space Res., 51 (2013) 905–911.
  15. D. Charchousi, V.K. Tsoukala, M.P. Papadopoulou, How evapotranspiration process may affect the estimation of water footprint indicator in agriculture?, Desal. Water Treat., 53 (2015) 3234–3243.
  16. A. Moghaddamnia, M. Ghafari Gousheh, J. Piri, S. Amin, D. Han, Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques, Adv. Water Resour., 32 (2009) 88–97.
  17. S.D. Khobragade, P. Semwal, A.R. Senthil Kumar, H.C. Nainwal, Pan coefficients for estimating open-water surface evaporation for a humid tropical monsoon climate region in India, J. Earth Syst. Sci., 128 (2019) 1–14.
  18. S. Karimi, O. Kisi, J. Shiri, O. Makarynskyy, Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia, Comput. Geosci., 52 (2013) 50–59.
  19. A. Yarar, M. Onucyildiz, N.K. Copty, Modelling level change in lakes using neuro-fuzzy and artificial neural networks, J. Hydrol., 365 (2009) 329–334.
  20. J. Sobhani, M. Najimi, Numerical study on the feasibility of dynamic evolving neural-fuzzy inference system for approximation of compressive strength of dry-cast concrete, Appl. Soft Comput. J., 24 (2014) 572–584.
  21. D.K. Roy, A. Lal, K.K. Sarker, K.K. Saha, B. Datta, Optimization algorithms as training approaches for prediction of reference evapotranspiration using adaptive neuro fuzzy inference system, Agric. Water Manage., 255 (2021) 107003, doi: 10.1016/j. agwat.2021.107003.
  22. Ö. Kişi, Daily pan evaporation modelling using a neuro-fuzzy computing technique, J. Hydrol., 329 (2006) 636–646.
  23. E.E. Başakın, Ö. Ekmekcioğlu, H. Çıtakoğlu, M. Özger, A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment, Neural Comput. Appl., 6 (2021) 783–812.
  24. H. Citakoglu, Comparison of multiple learning artificial intelligence models for estimation of long-term monthly temperatures in Turkey, Arabian J. Geosci., 14 (2021) 2131, doi: 10.1007/s12517-021-08484-3.
  25. E.E. Başakın, Ö. Ekmekcioğlu, M. Özger, N. Altınbaş, L. Şaylan, Estimation of measured evapotranspiration using datadriven methods with limited meteorological variables, Ital. J. Agrometeorol., 2021 (2021) 63–80.