Akademik Veri Yönetim Sistemi
POLISH JOURNAL OF ENVIRONMENTAL STUDIES, cilt.32, sa.4, ss.3161-3173, 2023 (SCI-Expanded)
Accurate and reliable peak discharge prediction is of great importance in water resource management
and flood control studies. The aim of this study was to predict monthly peak discharge by combining
various signal decomposition processes and machine learning models. For this purpose, monthly peak
discharge data were decomposed into subsignals utilizing Daubechies 3, Coiflet 5, discrete Meyer
main wavelets, and empirical mode decomposition methods, which are the ones most commonly used
in hydrological studies. The separated signals were subjected to correlation analysis, and the sum of
the highly correlated signals was presented as input to the machine learning models. Support vector
machines, regression trees, ensemble trees, and adaptive neuro-fuzzy inference system models were
used for peak discharge forecasting. The performance of the established models was evaluated with
the help of statistical indicators such as mean absolute error, root mean squared error, and determination
coefficient, and graphically through Taylor diagrams. At the end of the study, the most effective results
were obtained with the hybrid model established by the Daubechies 3 wavelet - 4 decomposition levels
and combined with the coarse Gaussian support vector machine.