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Dergiler / Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi / 2025 / Cilt: 42 Sayı: 3

Sınırlı meteorolojik veriler altında Günlük ETo değerlerinin makine öğrenmesi ile tahmini

Machine learning-based estimation of daily ETo under limited meteorological data

Ali Kaan Yetik(Niğde Ömer Halisdemir Üniversitesi)0000-0003-1372-8407
Sayfa
250–261
Yayın Tarihi
2025-12-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Sonuçlar, model performanslarında belirgin farklılıklar olduğunu ve ANN ile LGBM’nin çoğu senaryoda RFR’den daha başarılı sonuçlar verdiğini göstermektedir.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Referans bitki su tüketiminin (ETo) doğru şekilde tahmin edilmesi, özellikle su kıtlığı yaşayan bölgelerde, sürdürülebilir sulama yönetimi açısından kritik öneme sahiptir.
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Temel Bulgular & Veri:Bu çalışma, Türkiye’nin Alanya ilçesinde sınırlı meteorolojik veri koşulları altında günlük ETo tahmini için üç makine öğrenmesi modelinin: Yapay Sinir Ağları (ANN), Hafif Gradyan Hızlandırma Makineleri (LGBM) ve Rastgele Ormanı Regresyonu (RFR) performanslarını değerlendirmektedir. 1975–2023 yıllarını kapsayan veriler kullanılarak, sıcaklık, güneşlenme süresi, bağıl nem ve rüzgâr hızı gibi farklı meteorolojik değişkenlerin çeşitli kombinasyonlarından oluşan on farklı giriş senaryosu analiz edilmiştir.
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Sonuç & Katkı:Sonuçlar, model performanslarında belirgin farklılıklar olduğunu ve ANN ile LGBM’nin çoğu senaryoda RFR’den daha başarılı sonuçlar verdiğini göstermektedir.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2023 Verileri
⚙️Değişkenler:Yapay sinir ağları · gradyan hızlandırma · makine öğrenmesi

Özet

Referans bitki su tüketiminin (ETo) doğru şekilde tahmin edilmesi, özellikle su kıtlığı yaşayan bölgelerde, sürdürülebilir sulama yönetimi açısından kritik öneme sahiptir. Bu çalışma, Türkiye’nin Alanya ilçesinde sınırlı meteorolojik veri koşulları altında günlük ETo tahmini için üç makine öğrenmesi modelinin: Yapay Sinir Ağları (ANN), Hafif Gradyan Hızlandırma Makineleri (LGBM) ve Rastgele Ormanı Regresyonu (RFR) performanslarını değerlendirmektedir. 1975–2023 yıllarını kapsayan veriler kullanılarak, sıcaklık, güneşlenme süresi, bağıl nem ve rüzgâr hızı gibi farklı meteorolojik değişkenlerin çeşitli kombinasyonlarından oluşan on farklı giriş senaryosu analiz edilmiştir. Sonuçlar, model performanslarında belirgin farklılıklar olduğunu ve ANN ile LGBM’nin çoğu senaryoda RFR’den daha başarılı sonuçlar verdiğini göstermektedir. Tek değişkenli senaryolar arasında, sıcaklık temelli tahminler en güvenilir sonuçları vermiştir (R2=0.66). Sıcaklık ve güneşlenme süresinin birlikte kullanıldığı iki değişkenli senaryo, tahmin doğruluğunu belirgin şekilde artırmıştır (R2=0.85). En yüksek doğruluk ise sıcaklık, güneşlenme süresi ve rüzgâr hızının birlikte kullanıldığı üç değişkenli senaryo ile elde edilmiştir (R2=0.89). Bu çalışma, özellikle ANN ve LGBM modellerinin sınırlı meteorolojik veri koşullarında ETo tahmini için yüksek doğrulukla uygulanabileceğini ortaya koyarak, Akdeniz iklim kuşağında sürdürülebilir su yönetimi uygulamalarına önemli katkılar sunmaktadır.

Abstract

Accurate estimation of reference crop evapotranspiration (ETo) is essential for sustainable irrigation management, particularly in regions facing water scarcity challenges. This study evaluates the performance of three machine learning (ML) models: Artificial Neural Networks (ANN), Light Gradient Boosting Machines (LGBM), and Random Forest Regression (RFR) for estimating daily ETo in Alanya, Turkey, under varying scenarios of limited meteorological input availability. Ten input scenarios comprising different combinations of temperature, sunshine duration, relative humidity, and wind speed were analyzed using data spanning from 1975 to 2023. Results indicate significant variability in model performance, with ANN and LGBM consistently outperforming RFR across most scenarios. Among single-variable scenarios, temperature-based predictions were the most reliable (R2=0.66). Two variable scenarios combining temperature and sunshine duration notably enhanced prediction accuracy (R2=0.85). The highest predictive accuracy was achieved with a three-variable combination of temperature, sunshine duration, and wind speed (R2=0.89). This research underscores the potential of ML models, particularly ANN and LGBM, in accurately estimating ETo with limited meteorological data, contributing significantly to sustainable water management practices in Mediterranean climates.

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Bu Makaleye Atıf Yap

Yetik, A. K., *, A. K. Y. (2025). Sınırlı meteorolojik veriler altında Günlük ETo değerlerinin makine öğrenmesi ile tahmini. Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi, Cilt: 42 Sayı: 3, 250–261. https://doi.org/10.55507/gopzfd.1709027

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Kaynakça (60)

  1. Akar, F., Katipoğlu, O. M., Yeşilyurt, S. N., & Taş, M. B. H. (2023). Evaluation of tree-based machine learning and deep learning techniques in temperature-based potential evapotranspiration prediction. Polish J. Environ. Stud, 32, 1009-1023. http://doi.org/10.15244/pjoes/156927
  2. Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), D05109.
  3. Aly, M. S., Darwish, S. M., & Aly, A. A. (2024). High performance machine learning approach for reference evapotranspiration estimation. Stochastic Environmental Research and Risk Assessment, 38(2), 689-713. https://doi.org/10.1007/s00477-023-02594-y
  4. Amer, Z., & Farah, B. (2025). Evaporation forecasting using different machine learning models in Beni Haroun Dam, Algeria. Theoretical and Applied Climatology, 156(2), 121. https://doi.org/10.1007/s00704-024-05327-5
  5. Arslan, F., & Kartal, S. (2023). Water management effect on tropical fruits: Case study of Alanya, Turkey. Engineering For Rural Development, Jelgava, 533-538.
  6. Arslan, F., Alcon, F., Kartal, S., Erdoğan, K., & Zema, D. A. (2024). Sustainability of collective irrigation under water competition between agriculture and civil uses: The case study of Alanya Water Users Association (Türkiye). Agricultural Water Management, 306, 109167. https://doi.org/10.1016/j.agwat.2024.109167
  7. Baishnab, U., Hossen Sajib, M. S., Islam, A., Akter, S., Hasan, A., Roy, T., & Das, P. (2025). Deep learning approaches for short-crop reference evapotranspiration estimation: a case study in Southeastern Australia. Earth Science Informatics, 18(1), 1-17. https://doi.org/10.1007/s12145-024-01616-9
  8. Bijlwan, A., Pokhriyal, S., Ranjan, R., Singh, R. K., & Jha, A. (2024). Machine learning methods for estimating reference evapotranspiration. Journal of Agrometeorology, 26(1), 63-68. https://doi.org/10.54386/jam.v26i1.2462
  9. Breiman, L. (2001). Random forests. Machine learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
  10. Cemek, B., Tasan, S., Canturk, A., Tasan, M., & Simsek, H. (2023). Machine learning techniques in estimation of eggplant crop evapotranspiration. Applied Water Science, 13(6), 136. https://doi.org/10.1007/s13201-023-01942-1
  11. Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2(4), 303-314. https://doi.org/10.1007/BF02551274
  12. Deveci, H., Önler, B., & Erdem, T. (2025). Modeling the Effect of Soil Type Change on Irrigation Water Requirements of Sunflower and Wheat Using CROPWAT 8.0. Water, 17(10), 1437. https://doi.org/10.3390/w17101437
  13. Dimitriadou, S., & Nikolakopoulos, K. G. (2022). Artificial neural networks for the prediction of the reference evapotranspiration of the Peloponnese Peninsula, Greece. Water, 14(13), 2027. https://doi.org/10.3390/w14132027
  14. Elagib, N. A., & Musa, A. A. (2023). Correcting Hargreaves‐Samani formula using geographical coordinates and rainfall over different timescales. Hydrological Processes, 37(1), e14790. https://doi.org/10.1002/hyp.14790
  15. Elbeltagi, A., Deng, J., Wang, K., Malik, A., & Maroufpoor, S. (2020). Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment. Agricultural Water Management, 241, 106334. https://doi.org/10.1016/j.agwat.2020.106334
  16. Elbeltagi, A., Nagy, A., Mohammed, S., Pande, C. B., Kumar, M., Bhat, S. A.,. & Juhász, C. (2022). Combination of limited meteorological data for predicting reference crop evapotranspiration using artificial neural network method. Agronomy, 12(2), 516. https://doi.org/10.3390/agronomy12020516
  17. Emekli, Y., & Baştuğ, R. (2007). Antalya’da tarla koşullarinda bermuda çiminin su tüketimi ve bazi kiyas bitkİ su tüketimi eşitliklerinin geçerliliğinin belirlenmesi. Akdeniz University Journal of the Faculty of Agriculture, 20(1), 45-57.
  18. Fan, J., Ma, X., Wu, L., Zhang, F., Yu, X., & Zeng, W. (2019). Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data. Agricultural water management, 225, 105758. https://doi.org/10.1016/j.agwat.2019.105758
  19. Fan, J., Yue, W., Wu, L., Zhang, F., Cai, H., Wang, X.,. & Xiang, Y. (2018). Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China. Agricultural and forest meteorology, 263, 225-241. https://doi.org/10.1016/j.agrformet.2018.08.019
  20. Feng, Y., Cui, N., Zhao, L., Hu, X., & Gong, D. (2016). Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China. Journal of Hydrology, 536, 376-383. https://doi.org/10.1016/j.jhydrol.2016.02.053
  21. Feng, Y., Jia, Y., Cui, N., Zhao, L., Li, C., & Gong, D. (2017). Calibration of Hargreaves model for reference evapotranspiration estimation in Sichuan basin of southwest China. Agricultural Water Management, 181, 1-9. https://doi.org/10.1016/j.agwat.2016.11.010
  22. Ferreira, L. B., & da Cunha, F. F. (2020). New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning. Agricultural Water Management, 234, 106113. https://doi.org/10.1016/j.agwat.2020.106113
  23. Ferreira, L. B., da Cunha, F. F., de Oliveira, R. A., & Fernandes Filho, E. I. (2019). Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM–A new approach. Journal of Hydrology, 572, 556-570. https://doi.org/10.1016/j.jhydrol.2019.03.028
  24. Gong, D., Hao, W., Gao, L., Feng, Y., & Cui, N. (2021). Extreme learning machine for reference crop evapotranspiration estimation: Model optimization and spatiotemporal assessment across different climates in China. Computers and Electronics in Agriculture, 187, 106294. https://doi.org/10.1016/j.compag.2021.106294
  25. Gültaş, H. T., Ahi, Y., & Çakmak, B. (2025). Assessment of water resources status using the water footprint concept: The case of Tekirdağ province. Black Sea Journal of Agriculture, 8(2), 186-193. https://doi.org/10.47115/bsagriculture.1624100
  26. Granata, F. (2019). Evapotranspiration evaluation models based on machine learning algorithms—A comparative study. Agricultural Water Management, 217, 303-315. https://doi.org/10.1016/j.agwat.2019.03.015
  27. Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer. https://doi.org/10.1007/978-0-387-21606-5
  28. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5), 359-366. https://doi.org/10.1016/0893-6080(89)90020-8
  29. Karaca, C., Aydınşakir, K., Dinç, N., Büyüktaş, D., Baştuğ, R., & Polat, B. (2018). Comparison of Measured and Estimated Evapotranspiration of Pomegranate Grown Under Mediterranean Conditions. Ziraat Fakültesi Dergisi, 140-150.
  30. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W.,. & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.
  31. Liu, H., Li, Y., Josef, T., Zhang, R., & Huang, G. (2014). Quantitative estimation of climate change effects on potential evapotranspiration in Beijing during 1951–2010. Journal of Geographical Sciences, 24, 93-112. https://doi.org/10.1007/s11442-014-1075-5
  32. Liuzzo, L., Viola, F., & Noto, L. V. (2016). Wind speed and temperature trends impacts on reference evapotranspiration in Southern Italy. Theoretical and applied climatology, 123, 43-62. https://doi.org/10.1007/s00704-014-1342-5
  33. Mattar, M. A. (2018). Using gene expression programming in monthly reference evapotranspiration modeling: a case study in Egypt. Agricultural Water Management, 198, 28-38. https://doi.org/10.1016/j.agwat.2017.12.017
  34. Mehta, D., Das, P. P., Ghosh, S., Mishra, S., Alkhayyat, A., & Sharma, V. (2023). A normalized ANN model for earthquake estimation. In May 2023 2nd international conference on applied artificial intelligence and computing (ICAAIC) (pp. 151-155). IEEE.
  35. Nikolaou, G., Neocleous, D., Evangelides, E., & Kitta, E. (2025). A Decision Support System for Irrigation Scheduling Using a Reduced-Size Pan. Agronomy, 15(4), 848. https://doi.org/10.3390/agronomy15040848
  36. Niranjan, S., & Nandagiri, L. (2021). Effect of local calibration on the performance of the Hargreaves reference crop evapotranspiration equation. Journal of Water and Climate Change, 12(6), 2654-2673. https://doi.org/10.2166/wcc.2021.360
  37. Rajput, J., Singh, M., Lal, K., Khanna, M., Sarangi, A., Mukherjee, J., & Singh, S. (2024). Data-driven reference evapotranspiration (ET0) estimation: A comparative study of regression and machine learning techniques. Environment, Development and Sustainability, 26(5), 12679-12706. https://doi.org/10.1007/s10668-023-03978-4
  38. Rashid Niaghi, 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
  39. Santos, P. A. B. D., Schwerz, F., Carvalho, L. G. D., Baptista, V. B. D. S., Marin, D. B., Ferraz, G. A. E. S.,. & Bambi, G. (2023). Machine Learning and Conventional Methods for Reference Evapotranspiration Estimation Using Limited-Climatic-Data Scenarios. Agronomy, 13(9), 2366. https://doi.org/10.3390/agronomy13092366
  40. Sattari, M. T., Apaydin, H., Band, S. S., Mosavi, A., & Prasad, R. (2021). Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation. Hydrology and Earth System Sciences, 25(2), 603-618. https://doi.org/10.5194/hess-25-603-2021
  41. Sharma, G., Singh, A., & Jain, S. (2022). Hybrid deep learning techniques for estimation of daily crop evapotranspiration using limited climate data. Computers and Electronics in Agriculture, 202, 107338. https://doi.org/10.1016/j.compag.2022.107338
  42. Sun, X., Zhang, B., Dai, M., Gao, R., Jing, C., Ma, K.,. & Gu, X. (2024). Research on methods for estimating reference crop evapotranspiration under incomplete meteorological indicators. Frontiers in Plant Science, 15, 1354913. https://doi.org/10.3389/fpls.2024.1354913
  43. Tabari, H., Kisi, O., Ezani, A., & Talaee, P. H. (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. https://doi.org/10.1016/j.jhydrol.2012.04.007
  44. Taheri, M., Bigdeli, M., Imanian, H., & Mohammadian, A. (2025). An Overview of Evapotranspiration Estimation Models Utilizing Artificial Intelligence. Water, 17(9), 1384. https://doi.org/10.3390/w17091384
  45. Tang, D., Feng, Y., Gong, D., Hao, W., & Cui, N. (2018). Evaluation of artificial intelligence models for actual crop evapotranspiration modeling in mulched and non-mulched maize croplands. Computers and electronics in agriculture, 152, 375-384. https://doi.org/10.1016/j.compag.2018.07.029
  46. Tien Bui, D., Khosravi, K., Li, S., Shahabi, H., Panahi, M., Singh, V. P.,. & Bin Ahmad, B. (2018). New hybrids of anfis with several optimization algorithms for flood susceptibility modeling. Water, 10(9), 1210. https://doi.org/10.3390/w10091210
  47. Tikhamarine, Y., Malik, A., Souag-Gamane, D., & Kisi, O. (2020). Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration. Environmental Science and Pollution Research, 27, 30001-30019. https://doi.org/10.1007/s11356-020-08792-3
  48. TSMS. (2025). Turkish State Meteorological Service, climate data of long term season. Retrieved March 14, 2025, from https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx
  49. Turhan, A., Kuscu, H., & Asık, B. B. (2022). The influence of irrigation strategies on tomato fruit yield and leaf nutrient contents. Gesunde pflanzen, 74(4), 1021-1027. https://doi.org/10.1007/s10343-022-00678-3
  50. Ulbrich, U., Lionello, P., Belušić, D., Jacobeit, J., Knippertz, P., Kuglitsch, F. G.,. & Ziv, B. (2012). Climate of the Mediterranean: synoptic patterns, temperature, precipitation, winds, and their extremes.
  51. Usta, S. (2024). AutoCAD Yazılımı Kullanılarak Alansal ve Zamansal Bazlı İklim Sınıfı ve Referans Evapotranspirasyon Haritalarının Oluşturulması–Doğu Anadolu Bölgesi, Türkiye Örneği. Turkish Journal of Agriculture-Food Science and Technology, 12(7), 1213-1224. https://doi.org/10.24925/turjaf.v12i7.1213-1224.6689
  52. Valipour, M. (2015). Importance of solar radiation, temperature, relative humidity, and wind speed for calculation of reference evapotranspiration. Archives of Agronomy and Soil Science, 61(2), 239-255. https://doi.org/10.1080/03650340.2014.925107
  53. Wanniarachchi, S., & Sarukkalige, R. (2022). A review on evapotranspiration estimation in agricultural water management: Past, present, and future. Hydrology, 9(7), 123. https://doi.org/10.3390/hydrology9070123
  54. Wei, W., Ramalho, O., Malingre, L., Sivanantham, S., Little, J. C., & Mandin, C. (2019). Machine learning and statistical models for predicting indoor air quality. Indoor Air, 29(5), 704-726. https://doi.org/10.1111/ina.12580
  55. Wu, L., Peng, Y., Fan, J., & Wang, Y. (2019). Machine learning models for the estimation of monthly mean daily reference evapotranspiration based on cross-station and synthetic data. Hydrology Research, 50(6), 1730-1750. https://doi.org/10.2166/nh.2019.060
  56. Wu, T., Zhang, W., Jiao, X., Guo, W., & Hamoud, Y. A. (2020). Comparison of five Boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables. PLoS One, 15(6), e0235324. https://doi.org/10.1371/journal.pone.0235324
  57. 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
  58. Yong, S. L. S., Ng, J. L., Huang, Y. F., & Ang, C. K. (2023). Estimation of reference crop evapotranspiration with three different machine learning models and limited meteorological variables. Agronomy, 13(4), 1048. https://doi.org/10.3390/agronomy13041048
  59. Zhang, J., Ding, Y., Zhu, L., Wan, Y., Chai, M., & Ding, P. (2025). Estimating and forecasting daily reference crop evapotranspiration in China with temperature-driven deep learning models. Agricultural Water Management, 307, 109268. https://doi.org/10.1016/j.agwat.2024.109268
  60. Zhao, Y., Dong, H., Huang, W., He, S., & Zhang, C. (2024). Seamless terrestrial evapotranspiration estimation by machine learning models across the Contiguous United States. Ecological Indicators, 165, 112203. https://doi.org/10.1016/j.ecolind.2024.11