Atıf İçin Kopyala
Akar F., Katipoğlu O. M., Yeşilyurt S. N., Taş M. B. H.
Polish Journal of Environmental Studies, cilt.32, sa.2, ss.1-15, 2023 (SCI-Expanded)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
32
Sayı:
2
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Basım Tarihi:
2023
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Doi Numarası:
10.15244/pjoes/156927
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Dergi Adı:
Polish Journal of Environmental Studies
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Derginin Tarandığı İndeksler:
Science Citation Index Expanded (SCI-EXPANDED)
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Sayfa Sayıları:
ss.1-15
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Dokuz Eylül Üniversitesi Adresli:
Evet
Özet
In this study, Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Random
Forest (RF), Bagged Trees (BT), and Custom Deep Learning methods were used to estimate the potential
evapotranspiration (PET) values at Diyarbakir airport station in the Tigris basin. In establishing the
models, the average temperature, maximum temperature, minimum temperature, maximum wind
speed, relative humidity, average wind speed, and total precipitation values in the monthly time
period were chosen as inputs, and PET values were used as output. The data set is divided into 70%
training and 30% testing. 10-fold cross-validation to avoid overfitting problems. Training and test data
were randomly selected. The prediction performances of the models were evaluated according to the
statistical criteria of determination coefficient (R
2
), root mean square error (RMSE), mean absolute
error (MAE), and rank analysis. The best PET estimates were obtained using the inputs of mean, min,
maximum temperature, relative humidity, total precipitation, average, and maximum wind speed. It was
also concluded that XGBoost was the highest performance. When the R
2
values were examined, it was
seen that the Deep Learning model had higher performance. But for RMSE and MAE, XGBoost did
better. As a result of the rank analysis, it was seen that XGBoost got a higher score