ارزیابی و بهبود برآورد تبخیر- تعرق مرجع در شرایط محدودیت داده‌های هواشناسی با استفاده از الگوریتم جنگل تصادفی

3. نصرالهی، محمد، ذوالفقاری، علی اصغر و یزدانی، محمدرضا، 1401. تحلیل حساسیت تبخیر و تعرق مرجع در اقلیم مرطوب حاشیه جنوبی دریای خزر. تحقیقات کاربردی خاک، 10(2). 90-79.

4. یحیوی دیزج، آمنه، اکبری ازیرانی، طیبه، خالدی، شهریار و جوان، خدیجه، 1402. بررسی فصلی تبخیر تعرق مرجع و تحلیل حساسیت آن به عناصر هواشناسی در ایران. نشریه آب‌وخاک، 37(4). 657-643.

5. 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. Rome. Italy. 300 p.
6. Amani, S. and Shafizadeh-Moghadam, H. 2023. A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data. Agricultural Water Management, 284 (3), 108324.

 doi:10.1016/j.agwat.2023.108324

7. Bai, Y., Zhang, S., Bhattarai, N., Mallick, K., Liu, Q., Tang, L., Im, J., Guo, L. and Zhang, J. 2021. On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient. Agricultural and Forest Meteorology, 298-299, 108308.

doi:10.1016/j.agrformet.2020.108308

8. Bararkhanpour Ahmadi, S., Nadi, M., Mazloom Babanari, S. and Jedariforoughi, A. 2024. Analyzing the effect of climate change on the trend of extreme temperatures along the coast of Mazandaran province based on CMIP6 models. Journal of Water and Soil Conservation, 30(4), pp. 1-27. doi:10.22069/jwsc.2023.21185.3635
9. 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. doi:10.1016/j.agwat.2014.03.015
10. Chen, Z., Zhu, Z., Jiang, H. and Sun, S. 2020. Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods. Journal of Hydrology, 591, 125286.

 doi:10.1016/j.hydrol.2020.125286

11. Chu, R., Li, M., Shen, S., Islam, A.R.M.T., Cao, W., Tao, S. and Gao, P. 2017. Changes in reference evapotranspiration and its contributing factors in Jiangsu, a major economic and agricultural province of eastern China. Water, 9 (7). 486. doi:10.3390/w9070486
12. Clodoalves da Silva Júniora, J., Medeirosa, V., Garrozia, C., Montenegrob, A. and Gonçalvesa, G.E. 2019. Random forest techniques for spatial interpolation of evapotranspiration data from Brazilian’s northeast. Computers and Electronics in Agriculture, 166, 105017 ,pp. 105-116. doi:10.1016/j.compag.2019.105017
13. Feng, Y., Cui, N., Gong, D., Zhang, Q. and Zhao, L. 2017. Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Agricultural Water Management, 193, pp. 163-173.

doi:10.1016/j.agwat.2017.08.003

14. Goyal, P., Kumar, S. and Sharda, R. 2023. A review of the artificial intelligence (AI) based techniques for estimating reference evapotranspiration: Current trends and future perspectives. Computers and Electronics in Agriculture, 209,

doi:10.1016/co.mpag.2023.107836

15. Granata, F. and Di Nunno, F. 2021. Forcasting evapotranspiration in different climate using ensembles of recurrent neural networks. Agricultural Water Management, 255, 107040. doi:10.1016/j.agwat.2021.107040
16. Hargreaves, G.H. and Samani, Z.A. 1985. Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture, 1(2), pp. 96–99.

doi:10.13031/2013.26773

17. Lenhart, T., Eckhardt, K., Fohrer, N. and Frede, H.G. 2002. Comparison of two different approaches of sensitivity analysis. Physics and Chemistry of the Earth, 27, pp. 645–654.
18. Li, Y., Wang, W., Wang, G. and Tan, Q. 2022. Actual evapotranspiration estimation over the Tuojiang River Basin based on a hybrid CNN-RF model. Journal of Hydrology, 610, 127788. doi:10.1016/j.jhydrol.2022.127788
19. Liu, Y., Zhang, S., Zhang, J., Tang, L. and Bai, Y. 2021. Assessment and comparison of six machine learning models in estimating evapotranspiration over croplands using remote sensing and meteorological factors. Remote Sensing, 13(19), 3838.

doi:10.3390/rs13193838

20. Mandal, N. and Chanda, K. 2023. Performance of machine learning algorithms for multi-step ahead prediction of reference evapotranspiration across various agro-climatic zones and cropping seasons. Journal of Hydrology, 620, 129418.

doi:10.1016/j.jhydrol.2023.129418

21. Nouri, M., Homaee, M. and Bannayan, M. 2018. Spatiotemporal reference evapotranspiration changes in humid and semi-arid regions of Iran: Past trends and future projections. Theory of Applied Climatology, 133, pp. 361–375. doi:10.1007/s00704-017-2176-8
22. Priestley, C.H.B. and Taylor, R.J. 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100, pp. 81-92.
23. Qiu, R., Liu, C., Cui, N., Wu, Y., Wang, Z. and Li, G. 2019. Evapotranspiration estimation using a modified Priestley-Taylor model in a rice-wheat rotation system. Agricultural Water Management, 224, 105755. doi:10.1016/j.agwat.2019.105755
24. Rashid Niaghi, A., Hassanijalilian, O. and Shiri, J. 2021. Estimation of reference evapotranspiration using spatial and temporal machine learning approaches. Hydrology, 8(1), 25. https://doi.org/10.3390/hydrology8010025
25. Sharma, G., Singh, A. and Jain, S. 2022. A hybrid deep neural network approach to estimate reference evapotranspiration using limited climate data. Neural Computing and Application, 34, pp. 4013-4032. doi:10.1007/s00521-021-06661-9
26. Tabari, H., Marofi, S., Aeini, A., Talaee, P.H. and Mohammadi, K. 2011. Trend analysis of reference evapotranspiration in the western half of Iran. Agricultural and Forest Meteorology, 151(2), pp. 128–136. doi:10.1016/j.agrformet.2010.09.009
27. Tabari, H., Grismer, M.E. and Trajkovic, S. 2013. Comparative analysis of 31 reference evapotranspiration methods under humid conditions. Irrigation Sciences, 31, pp. 107-117. doi:10.1007/s00271-011-0295-z
28. Tunal, U., Tüzel, I.H., Tüzel, Y. and Şenol, Y. 2023. Estimation of actual crop evapotranspiration using artificial neural networks in tomato grown in closed soilless culture system. Agricultural Water Management, 284, 108331.

doi:10.1016/j.agwat.2023.108331

29. Valipour, M., Bateni, S.M., Gholami Sefidkouhi, M.A., Raeini-Sarjaz, M. and Singh, V.P. 2020. Complexity of forces driving trend of reference evapotranspiration and signals of climate change. Atmosphere, 11, pp. 1-26. doi:10.3390/atmos11101081
30. Veysi, S., Zarei, S., Galehban, E. and Tahooni, A. 2025. Leverafing artificial intelligence and machine-learning methods for bias correction of reference evapotraspiration utilizing ERA5 data. Irrigation and Drainage Engineering, 151(4), 106647.

doi:10.1061/jidedh.irenc-10487

31. Wang, S., Lian, J., Peng, Y., Hu, B. and Chen, H. 2019. Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China. Agricultural Water Management, 221, pp. 220-230. doi:10.1016/j.agwat.2019.03.027
32. Zhang, D., Liu, X. and Hong, H., 2013. Assessing the effect of climate change on reference evapotranspiration in China. Stochastic Environmental Research and Risk Assessment, 27(8), pp. 1871–1881. doi:10.1007/s00477-013-0723-0
33. Zhou, P., Liu, Z. and Cheng, L. 2020. An alternative approach for quantitatively estimating climate variability over China under the effects of ENSO events. Atmospheric Research, 238, 104897. doi: 1016/j.atmosres.2020.104897