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Modeling monthly reference evapotranspiration process in Turkey: application of machine learning methods

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Abstract

In this study, the predictive power of three different machine learning (ML)-based approaches, namely, multi-gene genetic programming (MGGP), M5 model trees (M5Tree), and K-nearest neighbor algorithm (KNN), for long-term monthly reference evapotranspiration (ET0) prediction were investigated. The input data consist of monthly solar radiation (Rs), maximum air temperature (Tmax), and wind speed (Ws) derived from 163 meteorological stations in Turkey. Different input combinations were created and analyzed. The model’s performance was evaluated using criteria such as Nash–Sutcliffe efficiency, Kling-Gupta efficiency, relative root mean squared error, mean absolute percentage error, and determination coefficient. Moreover, Taylor, radar, and boxplot diagrams were created. It was determined that the MGGP model outperformed both the M5Tree and the KNN models. The equation obtained from the MGGP model, for the best-performed combination of Rs-Tmax-Ws, was presented. The best weather conditions were obtained as 0.029 to 31.814 MJ/m2, − 5.8 to 45.7 °C, and 0.140 to 5.086 m/s for Rs, Tmax, and Ws, respectively. It was also found that the Rs was the most potent input variable for ET0 estimation while Ws was the weakest.

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Availability of data and materials

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

The codes were developed from the Matlab website.

References

  • Adnan, R. M., Parmar, K. S., Heddam, S., Shahid, S., & Kisi, O. (2021). Suspended sediment modeling using a heuristic regression method hybridized with Kmeans clustering. Sustainability, 13(9), 4648. https://doi.org/10.3390/su13094648

    Article  Google Scholar 

  • Ahmad S., Seonghoon K., Mohammad A., Junan S., & Yong B. (2022). Developing a prototype piezoelectric wafer-box for optimal energy harvesting. Journal of Civil Engineering and Architecture, 16(1), 1–12. https://doi.org/10.17265/1934-7359/2022.01.001

  • Ahvanooey, M. T., Li, Q., Wu, M., & Wang, S. (2019). A survey of genetic programming and its applications. KSII Transactions on Internet and Information Systems, 13(4). https://doi.org/10.3837/tiis.2019.04.002

  • Al-Mukhtar, M. (2021). Modeling of pan evaporation based on the development of machine learning methods. Theoretical and Applied Climatology, 146(3–4), 961–979. https://doi.org/10.1007/s00704-021-03760-4

    Article  CAS  Google Scholar 

  • Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Guidelines for computing crop water requirements. FAO. Rome: Food and Agriculture Organization. http://www.kimberly.uidaho.edu/water/fao56/fao56.pdf%5Cn, http://linkinghub.elsevier.com/retrieve/pii/S1161030110001103

  • Almorox, J., & Grieser, J. (2016). Calibration of the Hargreaves-Samani method for the calculation of reference evapotranspiration in different Köppen climate classes. Hydrology Research, 47(2), 521–531. https://doi.org/10.2166/nh.2015.091

    Article  Google Scholar 

  • Ananta, N., Nawin, R., Ravinesh, D., & Mumtaz, A. (2021). Development of data-driven models for wind speed forecasting in Australia. Predictive modelling for energy management and power systems engineering (pp. 143–190). Elsevier.

    Google Scholar 

  • Armstrong, R. A., Eperjesi, F., & Gilmartin, B. (2002). The application of analysis of variance (ANOVA) to different experimental designs in optometry. Ophthalmic and Physiological Optics, 22(3), 248–256. https://doi.org/10.1046/j.1475-1313.2002.00020.x

    Article  CAS  Google Scholar 

  • Badhiye, S. S., Sambhe, N. U., & Chatur, P. N. (2013). KNN technique for analysis and prediction of temperature and humidity data. International Journal of Computer Applications, 61(14), 7–13. https://doi.org/10.5120/9994-4847

    Article  Google Scholar 

  • Başakın, E. E., Ekmekcioğlu, Ö., Özger, M., Altınbaş, N., & Şaylan, L. (2021). Estimation of measured evapotranspiration using data-driven methods with limited meteorological variables. Italian Journal of Agrometeorology, (1), 63–80. https://doi.org/10.36253/ijam-1055

  • Batchelor, B. G. (1978). Pattern recognition: Ideas in practice. Pattern recognition: Ideas in practice. Plenum Press.

    Google Scholar 

  • Bayram, S., Ocal, M. E., Laptali Oral, E., & Atis, C. D. (2016). Comparison of multi layer perceptron (MLP) and radial basis function (RBF) for construction cost estimation: The case of Turkey. Journal of Civil Engineering and Management, 22(4), 480–490. https://doi.org/10.3846/13923730.2014.897988

    Article  Google Scholar 

  • Blaney, H. F., & Criddle, W. D. (1950). Determining water requirements in irrigated areas from climatological and irrigation data, Technical Bulletin US Soil Conservation Server. Washington Soil Conservation Service.

  • Carter, C., & Liang, S. (2019). Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing. International Journal of Applied Earth Observation and Geoinformation, 78, 86–92. https://doi.org/10.1016/j.jag.2019.01.020

  • Ceri, S., Bozzon, A., Brambilla, M., Della Valle, E., Fraternali, P., & Quarteroni, S. (2013). An introduction to information retrieval. Web information retrieval. Cambridge: Cambridge University Press. https://doi.org/10.1007/978-3-642-39314-3_1

  • Cheng, S., Jin, Y., Harrison, S. P., Quilodrán-Casas, C., Prentice, I. C., Guo, Y. -K., & Arcucci, R. (2022). Parameter flexible wildfire prediction using machine learning techniques: Forward and inverse modelling. Remote Sensing, 14(13), 3228. https://doi.org/10.3390/rs14133228

    Article  Google Scholar 

  • Cheng, W., Xi, W. J., & Celestin, S. (2021). Application of geodetector in sensitivity analysis of reference crop evapotranspiration spatial changes in Northwest China. Sciences in Cold and Arid Regions, 13(4), 314–325.

    Google Scholar 

  • Chhabra, A. (2018). Road traffic prediction using KNN and optimized multilayer perceptron. International Journal of Applied Engineering Research (Vol. 13). http://www.ripublication.com

  • Chia, M. Y., Huang, Y. F., & Koo, C. H. (2020). Support vector machine enhanced empirical reference evapotranspiration estimation with limited meteorological parameters. Computers and Electronics in Agriculture, 175, 105577. https://doi.org/10.1016/j.compag.2020.105577

    Article  Google Scholar 

  • Choi, H. I. L. (2022). Comment on Liu (2020): A rational performance criterion for hydrological model. Journal of Hydrology, 606, 126927. https://doi.org/10.1016/j.jhydrol.2021.126927

    Article  Google Scholar 

  • Citakoglu, H. (2017). Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey. Theoretical and Applied Climatology, 130(1–2). https://doi.org/10.1007/s00704-016-1914-7

  • Citakoglu, H. (2021). Comparison of multiple learning artificial intelligence models for estimation of long-term monthly temperatures in Turkey. Arabian Journal of Geosciences, 14(20). https://doi.org/10.1007/s12517-021-08484-3

  • Citakoglu, H., Babayigit, B., & Haktanir, N. A. (2020). Solar radiation prediction using multi-gene genetic programming approach. Theoretical and Applied Climatology, 142(3–4). https://doi.org/10.1007/s00704-020-03356-4

  • Citakoglu, H., Cobaner, M., Haktanir, T., & Kisi, O. (2014). Estimation of monthly mean reference evapotranspiration in Turkey. Water Resources Management, 28(1), 99–113. https://doi.org/10.1007/s11269-013-0474-1

    Article  Google Scholar 

  • Cobaner, M., Citakoǧlu, H., Haktanir, T., & Kisi, O. (2017). Modifying Hargreaves-Samani equation with meteorological variables for estimation of reference evapotranspiration in Turkey. Hydrology Research, 48(2). https://doi.org/10.2166/nh.2016.217

  • Dai, X., Shi, H., Li, Y., Ouyang, Z., & Huo, Z. (2009). Artificial neural network models for estimating regional reference evapotranspiration based on climate factors. Hydrological Processes, 23(3), 442–450. https://doi.org/10.1002/hyp.7153

    Article  Google Scholar 

  • Dasari, S. K., Lavesson, N., Andersson, P., & Persson, M. (2015). Tree-based response surface analysis. In International workshop on machine learning, optimization and big data (Springer, Cham., pp. 118–125).

  • Demir, V. (2022). Enhancing monthly lake levels forecasting using heuristic regression techniques with periodicity data component: Application of Lake Michigan. Theoretical and Applied Climatology, 148(3–4), 915–929. https://doi.org/10.1007/s00704-022-03982-0

    Article  Google Scholar 

  • Doorenbos, J., & Pruitt, W. O. (1984). Crop water requirements. FAO irrigation and drainage paper 24, FAO, Rome. (Vol. 21). Rome: FAO.

  • Dou, X., & Yang, Y. (2018). Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems. Computers and Electronics in Agriculture, 148, 95–106. https://doi.org/10.1016/j.compag.2018.03.010

    Article  Google Scholar 

  • Eastham, J., & Rose, C. W. (1988). Pasture evapotranspiration under varying tree planting density in an agroforestry experiment. Agricultural Water Management, 15(1), 87–105. https://doi.org/10.1016/0378-3774(88)90145-X

    Article  Google Scholar 

  • El-kenawy, E. -S.M., Zerouali, B., Bailek, N., Bouchouich, K., Hassan, M. A., Almorox, J., et al. (2022). Improved weighted ensemble learning for predicting the daily reference evapotranspiration under the semi-arid climate conditions. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-022-21410-8

    Article  Google Scholar 

  • Elbeltagi, A., Raza, A., Hu, Y., Al-Ansari, N., Kushwaha, N. L., Srivastava, A., et al. (2022). Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration. Applied Water Science, 12(7), 152. https://doi.org/10.1007/s13201-022-01667-7

    Article  Google Scholar 

  • Fan, J., Wu, L., Zheng, J., & Zhang, F. (2021). Medium-range forecasting of daily reference evapotranspiration across China using numerical weather prediction outputs downscaled by extreme gradient boosting. Journal of Hydrology, 601, 126664. https://doi.org/10.1016/j.jhydrol.2021.126664

    Article  Google Scholar 

  • Fredlund, D. G., Rahardjo, H., & Fredlund, M. D. (2012). Unsaturated soil mechanics in engineering practice. Unsaturated Soil Mechanics in Engineering Practice. https://doi.org/10.1002/9781118280492

    Article  Google Scholar 

  • Fu, T., Li, X., Jia, R., & Feng, L. (2021). A novel integrated method based on a machine learning model for estimating evapotranspiration in dryland. Journal of Hydrology, 603, 126881. https://doi.org/10.1016/j.jhydrol.2021.126881

    Article  Google Scholar 

  • Gandomi, A. H., & Alavi, A. H. (2012). A new multi-gene genetic programming approach to non-linear system modeling. Part II: Geotechnical and earthquake engineering problems. Neural Computing and Applications, 21(1), 189–201. https://doi.org/10.1007/s00521-011-0735-y

  • Gavili, S., Sanikhani, H., Kisi, O., & Mahmoudi, M. H. (2018). Evaluation of several soft computing methods in monthly evapotranspiration modelling. Meteorological Applications, 25(1), 128–138. https://doi.org/10.1002/met.1676

    Article  Google Scholar 

  • Ge, J., Zhao, L., Yu, Z., Liu, H., Zhang, L., Gong, X., & Sun, H. (2022). Prediction of greenhouse tomato crop evapotranspiration using XGBoost machine learning model. Plants, 11(15), 1923. https://doi.org/10.3390/plants11151923

    Article  Google Scholar 

  • Ghare, A. D., Porey, P. D., & Ingle, R. N. (2006). Discussion of “Simplified estimation of reference evapotranspiration from pan evaporation data in California” by Richard L. Snyder, Morteza Orang, Scott Matyac, and Mark E. Grismer. Journal of Irrigation and Drainage Engineering, 132(5), 519–520. https://doi.org/10.1061/(ASCE)0733-9437(2006)132:5(519)

  • Gocic, M., Petković, D., Shamshirband, S., & Kamsin, A. (2016). Comparative analysis of reference evapotranspiration equations modelling by extreme learning machine. Computers and Electronics in Agriculture, 127, 56–63. https://doi.org/10.1016/j.compag.2016.05.017

    Article  Google Scholar 

  • Gong, X., Qiu, R., Zhang, B., Wang, S., Ge, J., Gao, S., & Yang, Z. (2021). Energy budget for tomato plants grown in a greenhouse in northern China. Agricultural Water Management, 255, 107039. https://doi.org/10.1016/j.agwat.2021.107039

    Article  Google Scholar 

  • Goyal, R., Chandra, P., & Singh, Y. (2014). Suitability of KNN regression in the development of interaction based software fault prediction models. IERI Procedia, 6, 15–21. https://doi.org/10.1016/j.ieri.2014.03.004

    Article  Google Scholar 

  • Guitjens, J. C. (1982). Models of Alfalfa yield and evapotranspiration. In Journal of the Irrigation and Drainage Division, Proceedings of the American Society of Civil Engineers (pp. 212–222).

  • Gül, H. H., & Bayrak, H. (2022). Proposed tests for the general alternative in a mixed design consist of completely randomized and randomized block design. Afyon Kocatepe University Journal of Science and Engineering, 22(2022), 560–569.

    Article  Google Scholar 

  • Harbeck, G. E. (1962). A practical field technique for measuring reservoir evaporation utilizing mass-transfer theory. US Geological Survey professional paper (Vol. 272-E). http://pubs.usgs.gov/pp/0272e/report.pdf

  • Hargreaves, G. H., & Samani, Z. A. (1985). Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture, 1(2), 96–99. https://doi.org/10.13031/2013.26773

  • Hasan, R. A., Irshaid, H., Alhomaidat, F., Lee, S., & Oh, J.-S. (2022). Transportation mode detection by using smartphones and smartwatches with machine learning. KSCE Journal of Civil Engineering, 26(8), 3578–3589. https://doi.org/10.1007/s12205-022-1281-0

    Article  Google Scholar 

  • Heddam, S., & Kisi, O. (2018). Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree. Journal of Hydrology, 559, 499–509. https://doi.org/10.1016/j.jhydrol.2018.02.061

    Article  CAS  Google Scholar 

  • Hertz, T. (2006). 博士论文--Learning distance functions algorithms and Tomer_Hertz_2006.pdf. Hebrew University of Jerusalem.

  • Huang, G., Wu, L., Ma, X., Zhang, W., Fan, J., Yu, X., et al. (2019). Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions. Journal of Hydrology, 574, 1029–1041. https://doi.org/10.1016/j.jhydrol.2019.04.085

    Article  Google Scholar 

  • Huang, M., Lin, R., Huang, S., & Xing, T. (2017). A novel approach for precipitation forecast via improved K-nearest neighbor algorithm. Advanced Engineering Informatics, 33, 89–95. https://doi.org/10.1016/j.aei.2017.05.003

    Article  Google Scholar 

  • Huang, Y., & Li, S. E. (2021). Contribution analysis of meteorological factors on the variation of reference crop evapotranspiration in Minqin area. Journal of China Agricultural University, 26, 118–128.

    Google Scholar 

  • Imandoust, S. B., & Bolandraftar, M. (2013). Application of K-nearest neighbor (KNN) approach for predicting economic events: Theoretical background. International Journal of Engineering Research and Applications, 3(5), 605–610.

    Google Scholar 

  • Jayasree, P. K., Balan, K., & Rani, V. (2021). Water resources engineering. Practical civil engineering (Second Edi.). USA: John Wiley & Sons. https://doi.org/10.1201/9780429094811-14

  • Kisi, O. (2007). Evapotranspiration modelling from climatic data using a neural computing technique. Hydrological Processes, 21(14), 1925–1934. https://doi.org/10.1002/hyp.6403

    Article  Google Scholar 

  • Kisi, O., Sanikhani, H., Zounemat-Kermani, M., & Niazi, F. (2015). Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data. Computers and Electronics in Agriculture, 115, 66–77. https://doi.org/10.1016/j.compag.2015.04.015

    Article  Google Scholar 

  • Kisi, O., & Demir, V. (2016). Evapotranspiration estimation using six different multi-layer perceptron algorithms. Irrigation & Drainage Systems Engineering, 5(2). https://doi.org/10.4172/2168-9768.1000164

  • Kisi, O., & Kilic, Y. (2016). An investigation on generalization ability of artificial neural networks and M5 model tree in modeling reference evapotranspiration. Theoretical and Applied Climatology, 126(3–4), 413–425. https://doi.org/10.1007/s00704-015-1582-z

    Article  Google Scholar 

  • Kisi, O., Demir, V., & Kim, S. (2017a). Estimation of long-term monthly temperatures by three different adaptive neuro-fuzzy approaches using geographical inputs. Journal of Irrigation and Drainage Engineering, 143(12), 04017052. https://doi.org/10.1061/(asce)ir.1943-4774.0001242

    Article  Google Scholar 

  • Kisi, O., Shiri, J., & Demir, V. (2017b). Hydrological time series forecasting using three different heuristic regression techniques. In Handbook of neural computation (pp. 45–65). Elsevier. https://doi.org/10.1016/B978-0-12-811318-9.00003-X

  • Kisi, O., Keshtegar, B., Zounemat-Kermani, M., Heddam, S., & Trung, N.-T. (2021). Modeling reference evapotranspiration using a novel regression-based method: Radial basis M5 model tree. Theoretical and Applied Climatology, 145(1–2), 639–659. https://doi.org/10.1007/s00704-021-03645-6

    Article  Google Scholar 

  • Kling, H., Fuchs, M., & Paulin, M. (2012). Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. Journal of Hydrology, 424–425, 264–277. https://doi.org/10.1016/j.jhydrol.2012.01.011

    Article  Google Scholar 

  • Khoob, A. R. (2008). Comparative study of Hargreaves’s and artificial neural network’s methodologies in estimating reference evapotranspiration in a semiarid environment. Irrigation Science, 26(3), 253–259. https://doi.org/10.1007/s00271-007-0090-z

    Article  Google Scholar 

  • Landwehr, N., Hall, M., & Frank, E. (2005). Logistic model trees. Machine Learning, 59(1–2), 161–205. https://doi.org/10.1007/s10994-005-0466-3

    Article  Google Scholar 

  • Lewis, C. D. (1982). A radical guide to exponential smoothing and curve fitting. Butterworth-Heinemann.

    Google Scholar 

  • Liu, W., Zhang, B., & Han, S. (2020). Quantitative analysis of the impact of meteorological factors on reference evapotranspiration changes in Beijing, 1958–2017. Water, 12(8), 2263. https://doi.org/10.3390/w12082263

    Article  Google Scholar 

  • López-Urrea, R., de Olalla, F. M., & S., Fabeiro, C., & Moratalla, A. (2006). An evaluation of two hourly reference evapotranspiration equations for semiarid conditions. Agricultural Water Management, 86(3), 277–282. https://doi.org/10.1016/j.agwat.2006.05.017

    Article  Google Scholar 

  • Luo, Y., Gao, P., & Mu, X. (2021). Influence of meteorological factors on the potential evapotranspiration in Yanhe River Basin. China. Water, 13(9), 1222. https://doi.org/10.3390/w13091222

    Article  Google Scholar 

  • Lurie, M., & Michailoff, N. (1936). Evaporation from free water surface. In Industrial and engineering chemistry (Vol. 28, pp. 345–349). https://doi.org/10.1021/ie50315a019

  • Makkink, G. F. (1957). Testing the Penman formula by means of lysimeters. Journal of the Institution of Water Engineers, 11, 277–288.

    Google Scholar 

  • Marsland, S. (2015). Machine learning: An algorithmic perspective. Taylor & Francis.

    Google Scholar 

  • McCuen, R. H. (2004). Hydrologic analysis and design. Journal of the American Water Resources Association, 40(3), 838.

    Google Scholar 

  • Michalski, R. S., Stepp, R. E., & Diday, E. (1981). A recent advance in data analysis: Clustering objects into classes characterized by conjunctive concepts. Progress in pattern recognition. North-Holland, Amsterdam. https://doi.org/10.1016/b978-0-444-86325-6.50005-9

  • Mittal, K., Aggarwal, G., & Mahajan, P. (2019). Performance study of K-nearest neighbor classifier and K-means clustering for predicting the diagnostic accuracy. International Journal of Information Technology, 11(3), 535–540. https://doi.org/10.1007/s41870-018-0233-x

    Article  Google Scholar 

  • Mohammadrezapour, O., Piri, J., & Kisi, O. (2019). Comparison of SVM, ANFIS and GEP in modeling monthly potential evapotranspiration in an arid region (case study: Sistan and Baluchestan Province, Iran). Water Supply, 19(2), 392–403. https://doi.org/10.2166/ws.2018.084

    Article  Google Scholar 

  • Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3), 282–290. https://doi.org/10.1016/0022-1694(70)90255-6

    Article  Google Scholar 

  • Niazkar, M. (2019). Revisiting the estimation of Colebrook friction factor: A comparison between artificial intelligence models and C-W based explicit equations. KSCE Journal of Civil Engineering, 23(10), 4311–4326. https://doi.org/10.1007/s12205-019-2217-1

    Article  Google Scholar 

  • Niazkar, M., & Niazkar, H. R. (2020). COVID-19 outbreak: Application of multi-gene genetic programming to country-based prediction models. Electronic Journal of General Medicine, 17(5), em247. https://doi.org/10.29333/ejgm/8232

  • Niazkar, M., Talebbeydokhti, N., & Afzali, S. -H. (2020). Bridge backwater estimation: A comparison between artificial intelligence models and explicit equations. Scientia Iranica, 0–0. https://doi.org/10.24200/sci.2020.51432.2175

  • Niazkar, M., Talebbeydokhti, N., & Afzali, S. H. (2019). Novel grain and form roughness estimator scheme incorporating artificial intelligence models. Water Resources Management, 33(2), 757–773. https://doi.org/10.1007/s11269-018-2141-z

    Article  Google Scholar 

  • Niaghi, R. A., Hassanijalilian, O., & Shiri, J. (2021). Estimation of reference evapotranspiration using spatial and temporal machine learning approaches. Hydrology, 8(1), 25. https://doi.org/10.3390/hydrology8010025

    Article  Google Scholar 

  • Noh, H., Kwon, S., Seo, I. W., Baek, D., & Jung, S. H. (2020). Multi-gene genetic programming regression model for prediction of transient storage model parameters in natural rivers. Water, 13(1), 76. https://doi.org/10.3390/w13010076

    Article  Google Scholar 

  • Nourani, V., Elkiran, G., & Abdullahi, J. (2019). Multi-station artificial intelligence based ensemble modeling of reference evapotranspiration using pan evaporation measurements. Journal of Hydrology, 577, 123958. https://doi.org/10.1016/j.jhydrol.2019.123958

    Article  Google Scholar 

  • Pal, M., & Deswal, S. (2009). M5 model tree based modelling of reference evapotranspiration. Hydrological Processes, 23(10), 1437–1443. https://doi.org/10.1002/hyp.7266

    Article  Google Scholar 

  • Parajuli, P. B., Jayakody, P., & Ouyang, Y. (2018). Evaluation of using remote sensing evapotranspiration data in SWAT. Water Resources Management, 32(3), 985–996. https://doi.org/10.1007/s11269-017-1850-z

    Article  Google Scholar 

  • Park, J. S., Ren, Q., Chen, Y., Cluckie, I. D., Butts, M., & Graham, D. (2009). Effectiveness of complex physics and DTM-based distributed models for flood risk management of the River Tone (IAHS., Vol. 331). UK: IAHS.

  • Penman, H. L. (1948). Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 193(1032), 120–145. https://doi.org/10.1098/rspa.1948.0037

  • Peters, E. B., Hiller, R. V., & McFadden, J. P. (2011). Seasonal contributions of vegetation types to suburban evapotranspiration. Journal of Geophysical Research, 116(G1), G01003. https://doi.org/10.1029/2010JG001463

    Article  Google Scholar 

  • Prasad, D., Goyal, S. K., Sharma, A., Bindal, A., & Kushwah, V. S. (2019). System model for prediction analytics using K-nearest neighbors algorithm. Journal of Computational and Theoretical Nanoscience, 16(10), 4425–4430. https://doi.org/10.1166/jctn.2019.8536

    Article  CAS  Google Scholar 

  • Priestley, C. H. B., & Taylor, R. J. (1972). On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100(2), 81–92. https://doi.org/10.1175/1520-0493(1972)100%3c0081:OTAOSH%3e2.3.CO;2

    Article  Google Scholar 

  • Quinlan, J. R. (1992). Learning with continuous classes. Australian joint conference on artificial intelligence. World Scientiic.

  • Rao, K., & D. V. S. K., Premalatha, M., & Naveen, C. (2018). Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: A case study. Renewable and Sustainable Energy Reviews, 91, 248–258. https://doi.org/10.1016/j.rser.2018.03.096

    Article  Google Scholar 

  • Raza, A., Shoaib, M., Faiz, M. A., Baig, F., Muneer Khan, M., Kaleem Ullah, M., & Zubair, M. (2020a). Comparative assessment of reference evapotranspiration estimation using conventional method and machine learning algorithms in four climatic regions. Pure and Applied Geophysics, 177(9), 4479–4508. https://doi.org/10.1007/s00024-020-02473-5

    Article  Google Scholar 

  • Raza, A., Shoaib, M., Khan, A., Baig, F., Faiz, M. A., & Khan, M. M. (2020b). Application of non-conventional soft computing approaches for estimation of reference evapotranspiration in various climatic regions. Theoretical and Applied Climatology, 139(3–4), 1459–1477. https://doi.org/10.1007/s00704-019-03007-3

    Article  Google Scholar 

  • Rodrigues, G. C., & Braga, R. P. (2021). A simple application for computing reference evapotranspiration with various levels of data availability—ETo tool. Agronomy, 11(11), 2203. https://doi.org/10.3390/agronomy11112203

    Article  Google Scholar 

  • Saggi, M. K., & Jain, S. (2019). Reference evapotranspiration estimation and modeling of the Punjab Northern India using deep learning. Computers and Electronics in Agriculture, 156, 387–398. https://doi.org/10.1016/j.compag.2018.11.031

    Article  Google Scholar 

  • Sahoo, A., & Ghose, K. (2022). Imputation of missing precipitation data using KNN, SOM, RF, and FNN. Soft Computing, 26, 5919–5936. https://doi.org/10.1007/s00500-022-07029-4

    Article  Google Scholar 

  • Sanikhani, H., Kisi, O., Maroufpoor, E., & Yaseen, Z. M. (2019). Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: Application of different modeling scenarios. Theoretical and Applied Climatology, 135(1–2), 449–462. https://doi.org/10.1007/s00704-018-2390-z

    Article  Google Scholar 

  • Searson, D. P. (2009). GPTIPS: Genetic programming and symbolic regression for MATLAB.

  • Searson, D. P., Leahy, D. E., & Willis, M. J. (2010). GPTIPS: An open source genetic programming toolbox for multigene symbolic regression. In Proceedings of the international multiconference of engineers and computer scientists Citeseer (pp. 77–80).

  • Senay, G. B., Verdin, J. P., Lietzow, R., & Melesse, A. M. (2008). Global daily reference evapotranspiration modeling and evaluation. Journal of the American Water Resources Association, 44(4), 969–979. https://doi.org/10.1111/j.1752-1688.2008.00195.x

    Article  Google Scholar 

  • Serengil, Y. (2018). Climate change and carbon management. Ankara: UNDP.

  • Shanker, M., Hu, M. Y., & Hung, M. S. (1996). Effect of data standardization on neural network training. Omega, 24(4), 385–397. https://doi.org/10.1016/0305-0483(96)00010-2

    Article  Google Scholar 

  • Shiri, J. (2019). Modeling reference evapotranspiration in island environments: Assessing the practical implications. Journal of Hydrology, 570, 265–280. https://doi.org/10.1016/j.jhydrol.2018.12.068

    Article  Google Scholar 

  • Shiri, J., Keshavarzi, A., Kisi, O., & Karimi, S. (2017). Using soil easily measured parameters for estimating soil water capacity: soft computing approaches. Computers and Electronics in Agriculture, 141, 327–339.

  • Shiri, J., Nazemi, A. H., Sadraddini, A. A., Landeras, G., Kisi, O., Fakheri Fard, A., & Marti, P. (2014). Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran. Computers and Electronics in Agriculture, 108, 230–241. https://doi.org/10.1016/j.compag.2014.08.007

    Article  Google Scholar 

  • Shiri, J., Sadraddini, A. A., Nazemi, A. H., Kisi, O., Marti, P., Fard, A. F., & Landeras, G. (2013). Evaluation of different data management scenarios for estimating daily reference evapotranspiration. Hydrology Research, 44(6), 1058–1070. https://doi.org/10.2166/nh.2013.154

    Article  Google Scholar 

  • Singh, K. K., Pal, M., & Singh, V. P. (2010). Estimation of mean annual flood in Indian catchments using backpropagation neural network and M5 model tree. Water Resources Management, 24(10), 2007–2019. https://doi.org/10.1007/s11269-009-9535-x

    Article  Google Scholar 

  • Snyder, R., & Pruitt, W. (1985). Estimating reference evapotranspiration with hourly data. In California irrigation management information system final report. Univ. of California-Davis. Land, air and water resources paper (p. 10013).

  • Snyder, R. L., Orang, M., Matyac, S., & Grismer, M. E. (2005). Simplified estimation of reference evapotranspiration from pan evaporation data in California. Journal of Irrigation and Drainage Engineering, 131(3), 249–253. https://doi.org/10.1061/(ASCE)0733-9437(2005)131:3(249)

    Article  Google Scholar 

  • Su, Y. Y., & Fan, X. K. (2020). Research and analysis of main meteorological factors affecting evapotranspiration based on weighing method. Agricultural Research in the Arid Areas, 38, 40–48.

    Google Scholar 

  • Tomas‐Burguera, M., Beguería, S., & Vicente‐Serrano, S. M. (2021). Climatology and trends of reference evapotranspiration in Spain. International Journal of Climatology, 41(S1). https://doi.org/10.1002/joc.6817

  • Torres, A. F., Walker, W. R., & McKee, M. (2011). Forecasting daily potential evapotranspiration using machine learning and limited climatic data. Agricultural Water Management, 98(4), 553–562. https://doi.org/10.1016/j.agwat.2010.10.012

    Article  Google Scholar 

  • Trajkovic, S. (2005). Temperature-based approaches for estimating reference evapotranspiration. Journal of Irrigation and Drainage Engineering, 131(4), 316–323. https://doi.org/10.1061/(ASCE)0733-9437(2005)131:4(316)

    Article  Google Scholar 

  • Turkish Ministry of Environment and Forestry. (2009). UN convention of biological diversity fourth national report. Republic of Turkey, Ministry of Environment and Forestry. Fourth national report.

  • Uncuoglu, E., Citakoglu, H., Latifoglu, L., Bayram, S., Laman, M., Ilkentapar, M., & Oner, A. A. (2022). Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems. Applied Soft Computing, 129, 109623.

  • Valipour, M. (2017). Analysis of potential evapotranspiration using limited weather data. Applied Water Science, 7(1), 187–197. https://doi.org/10.1007/s13201-014-0234-2

    Article  CAS  Google Scholar 

  • Wang, J., Raza, A., Hu, Y., Buttar, N. A., Shoaib, M., Saber, K., et al. (2022). Development of monthly reference evapotranspiration machine learning models and mapping of Pakistan—A comparative study. Water, 14(10), 1666. https://doi.org/10.3390/w14101666

    Article  Google Scholar 

  • Wang, S., Fu, Z., Chen, H., Nie, Y., & Wang, K. (2016). Modeling daily reference ET in the karst area of northwest Guangxi (China) using gene expression programming (GEP) and artificial neural network (ANN). Theoretical and Applied Climatology, 126(3–4), 493–504. https://doi.org/10.1007/s00704-015-1602-z

    Article  Google Scholar 

  • Wang, Y., & Witten, I. H. (1997). Inducing model trees for continuous classes. European conference on machine learning (ECML). http://www.cs.waikato.ac.nz/~ml/publications/1997/Wang-Witten-Induct.pdf

  • Xu, C.-Y., & Singh, V. P. (2002). Cross comparison of empirical equations for calculating potential evapotranspiration with data from Switzerland. Water Resources Management, 16(3), 197–219. https://doi.org/10.1023/A:1020282515975

    Article  Google Scholar 

  • Xu, D., Wang, Y., Peng, P., Beilun, S., Deng, Z., & Guo, H. (2020). Real-time road traffic state prediction based on kernel-KNN. Transportmetrica a: Transport Science, 16(1), 104–118. https://doi.org/10.1080/23249935.2018.1491073

    Article  Google Scholar 

  • Yamaç, S. S., & Todorovic, M. (2020). Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data. Agricultural Water Management, 228, 105875. https://doi.org/10.1016/j.agwat.2019.105875

    Article  Google Scholar 

  • Yi, H. -S., Lee, B., Park, S., Kwak, K. -C., & An, K. -G. (2018). Prediction of short-term algal bloom using the M5P model-tree and extreme learning machine. Environmental Engineering Research, 24(3), 404–411. https://doi.org/10.4491/eer.2018.245

    Article  Google Scholar 

  • Yurtseven, I., & Serengil, Y. (2021). Comparison of different empirical methods and data-driven models for estimating reference evapotranspiration in semi-arid Central Anatolian Region of Turkey. Arabian Journal of Geosciences, 14(19), 2033. https://doi.org/10.1007/s12517-021-08150-8

    Article  Google Scholar 

  • Zhang, Q., Barri, K., Jiao, P., Salehi, H., & Alavi, A. H. (2021). Genetic programming in civil engineering: Advent, applications and future trends. Artificial Intelligence Review, 54(3), 1863–1885. https://doi.org/10.1007/s10462-020-09894-7

    Article  Google Scholar 

  • Zotarelli, L., Dukes, M. D., Romero, C. C., Migliaccio, K. W., & Morgan, K. T. (2014). Step by step calculation of the Penman-Monteith evapotranspiration (FAO-56 method). AE459. Institute of Food and Agricultural Sciences. University of Florida.

  • Zouzou, Y., & Çıtakoğlu, H. (2021). Reference evapotranspiration prediction from limited climatic variables using support vector machines and Gaussian processes. European Journal of Science and Technology. https://doi.org/10.31590/ejosat.999319

  • Zouzou, Y., & Citakoglu, H. (2022). General and regional cross-station assessment of machine learning models for estimating reference evapotranspiration. Acta Geophysica, 1–21.

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Acknowledgements

The authors thank the Turkish State Meteorological Service (MGM) for the statistics provided.

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  1. Department of Civil Engineering, Erciyes University, Kayseri, Türkiye

    Savaş Bayram & Hatice Çıtakoğlu

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  1. Savaş Bayram
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Conceptualization: SB; methodology: HC; data collection: HC; analysis: HC; writing—original draft preparation: SB, HC; writing—review and editing: SB; supervision: SB, HC.

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Correspondence to Hatice Çıtakoğlu.

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Bayram, S., Çıtakoğlu, H. Modeling monthly reference evapotranspiration process in Turkey: application of machine learning methods. Environ Monit Assess 195, 67 (2023). https://doi.org/10.1007/s10661-022-10662-z

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