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

Öz

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.

Anahtar Kelimeler

• Artificial neural networks
• gradient boosting
• machine learning
• random forest
• reference crop evapotranspiration

Etik Beyan

There is no need to obtain permission from the ethics committee for this study.

Sınırlı meteorolojik veriler altında Günlük ETo değerlerinin makine öğrenmesi ile tahmini

Öz

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.

Anahtar Kelimeler

• Yapay sinir ağları
• gradyan hızlandırma
• makine öğrenmesi
• rastgele orman
• referans bitki su tüketimi

Kaynakça

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