Solar radiation, which is used in hydrological modeling, agricultural, solar
energy systems, and climatological studies, is the most important element of the energy
reaching the earth. The present study compared, the performance of two empirical
equations -Angstrom and Hargreaves-Samani equations- and, three machine learning models
-Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Long Short-Term
Memory (LSTM)-. Various learning models were developed for the variables used in each
empirical equation. In the present study, monthly data of six stations in Turkey, three
stations receiving the most solar radiation and three stations receiving the least solar
radiation, were used. In terms of the mean squared error (MSE), root mean squared error
(RMSE), mean absolute error (MAE), and determination coefficient () values of each
model, LSTM was the most successful model, followed by ANN and SVM. The MAE value was
2.65 with the Hargreaves-Samani equation and, decreased to 0.987 with the LSTM model
while MAE was 1.24 in the Angstrom equation and decreased to 0.747 with the LSTM model.
The study revealed that the deep learning model is more appropriate to use compared to
the empirical equations even in cases where there is limited data.
In the study, variability in reference evapotranspiration (ET0) from Southeastern Anatolian Project (GAP) area was investigated with linear regression method. For the purpose, seasonal ET0 time series were formed from monthly reference evapotranspiration (ET0). The ET0 data sets of three sites showed a statistically significance decreasing trend while there was upward trend in some seasons of two sites. But, variation in all seasonal ET0 time series of Kilis site was not detected.
Parametric approaches in statistical analysis assume that any given data are normally distributed. Therefore, the test of whether this conventional assumption is valid should be made in this context of the available data’s normality before being passed to the application of statistical tests. The paper is focused on the normality methodologies commonly used in literature, named Kolmogorov-Smirnov, Jarque-Bera, D’agostino, Anderson Darling, Shapiro-Wilk and Ryan Joiner. In the study, the seasonal maximum data from eight streamflow gauging stations in Yesilirmak Basin was used as material. The normality in the 59% of the whole data sets were obtained as the highest result by the Kolmogorov –Smirnov approach, when compared to the other normality tests considered in the study.
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