International Joint Conference on Engineering, Science and Artificial Intelligence, Balıkesir, Türkiye, 15 - 17 Haziran 2022, ss.180
This study aimed to estimate monthly
evapotranspiration (ET) values in Hakkâri province by combining support vector
regression, bagged tree, and boosted tree methods with wavelet transform. For
this purpose, precipitation, runoff, surface net solar radiation, air
temperatures, and previous ET values were divided into sub-signals with various
mother wavelets such as Daubechies 4, Meyer, and Symlet 2 and presented as
input to machine learning (ML) algorithms. While establishing the models, the
data were divided into 80% (413 data) training and 20% (103 data) testing. The
models' performances were made according to the widely used root mean square
error, mean average error, determination coefficient, and Taylor diagrams. As a
result of the study, it was revealed that the hybrid wavelet ML, which is
established with input combinations separated into subcomponents by wavelet
transform, generally produces more successful predictions than the stand-alone
ML model. In addition, it was revealed that the optimum ET forecasting model
was obtained with the wavelet bagged tree algorithm with Symlet 2 mother
wavelet.