Yarı nemli bir bölgede günlük referans bitki su tüketiminin tahmini için üç farklı makine öğrenimi modellerinin geliştirilmesi ve karşılaştırılması

Su kaynaklarının planlanması ve tarımsal su yönetimi için referans bitki su tüketiminin (ET0) doğru tahmin edilmesi oldukça önemlidir. Bu çalışmada üç farklı makine öğrenimi yönteminin, çok değişkenli uyarlanabilir regresyon eğrileri (MARS), rassal ağaç (RT) ve gauss süreç regresyonu (GPR), yarı nemli iklim koşullarına sahip bir bölgeden alınan iklim verileri kullanılarak günlük ET0’ı tahmin etme performansları araştırılmıştır. Modelleri eğitmek ve test etmek için Bafra ilçesinde 2018–2020 döneminde toplanan günlük meteorolojik verilerin beş girdi kombinasyonu [günlük ortalama (Tort), maksimum (Tmax) ve minimum sıcaklık (Tmin), rüzgar hızı (u2), bağıl nem (RHort) ve güneşlenme şiddeti (Rs) hem tam hem de eksik kombinasyonları dahil] kullanılmıştır. Makine öğrenimi modellerinin performansı ise FAO-56 Penman Monteith (FAO-56 PM) standart denklemi ile karşılaştırılmıştır. Modellerin doğruluğunu değerlendirmek için dört farklı istatistiksel performans indeksi kullanılmıştır [belirleme katsayısı (R2), ortalama mutlak hata (MAE), hata kareleri toplamının karekökü (RMSE) ve Nash–Sutcliffe etkinlik katsayısı (NSE)]. Sonuçlar, genel olarak MARS modellerinin RT ve GPR modellerinden daha iyi performans gösterdiğini ortaya koymuştur. Tüm verilerin girdi olarak kullanıldığı beşinci senaryo için MARS modeli tarafından tahmin edilen ET0 değerlerinin FAO-56 PM değerleri ile iyi bir uyum içinde olduğu belirlenmiştir (R2=0.982, MAE=0.250, RMSE=0.305, NSE=0.965). Bununla birlikte meteorolojik veriler tam olmadığında bile sadece Tort, RHort ve Rs ile oldukça yüksek günlük ET0 tahminleri elde edilmiştir. Genel olarak, radyasyona dayalı makine öğrenimi modelleri, sıcaklığa dayalı makine öğrenimi modellerinden daha iyi performans göstermiştir. Sonuçlar, yarı nemli iklim özelliklerine sahip bir bölgede MARS modelinin ET0'ı oldukça verimli ve doğru bir şekilde modellemek için etkili bir şekilde kullanılabileceğini göstermektedir.

Anahtar Kelimeler:

Çok değişkenli uyarlanabilir regresyon eğrileri, Modelleme, Rassal ağaç, Referans bitki su tüketimi

Development and comparison of three different machine learning models for estimating daily reference evapotranspiration in sub-humid region

Accurate estimation of reference evapotranspiration (ET0) is very important for water resource planning and agricultural water management. This study investigates the performance of three different machine learning methods, multivariate adaptive regression splines (MARS), random tree (RT), and gaussian process regression (GPR), for predicting daily ET0 using climate data from a sub-humid climate region. Five input combinations [including both complete and incomplete combinations of daily average (Tavg), maximum (Tmax) and minimum (Tmin) temperature wind speed (u2), relative humidity (RHavg), and solar radiation (Rs)] of daily meteorological data collected in Bafra district during the 2018–2020 period were used for model training and testing. The performance of machine learning models was compared with the standard FAO-56 Penman-Monteith equation (FAO-56 PM). Four different statistical performance indices were used to evaluate the accuracy of the models [coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE)]. The results revealed that MARS models outperform the RT and GPR models. For scenario 5, using all data as input, the ET0 values estimated by the MARS models agreed well with the FAO-56 PM values (R2=0.982, MAE=0.250, RMSE=0.305, NSE=0.965). However, even if the meteorological data are incomplete, very high daily ET0 estimates have been obtained using only Tavg, RHavg, and Rs. Overall, the radiation-based machine learning models outperformed the temperature-based machine learning models. The results show that the MARS model can be used effectively to model ET0 quite efficiently and accurately in a region with sub-humid climate characteristics.

Keywords:

Multivariate adaptive regression splines, Modeling, Random tree, Reference evapotranspiration,

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