The development of various models to estimate hydrological variables, such as precipitation and runoff is significant regarding handling the water-related problems in the future. This study investigates the performances of Artificial Neural Network (ANN), Auto-Regressive Integrated Moving Average (ARIMA), Wavelet-ARIMA (WARIMA), and WARIMA-ANN models for monthly streamflow forecasting. These models were utilized in two stations of the Susurluk basin in Turkey. In this regard, first, the streamflow data were decomposed into components by wavelet transformation for the WARIMA and WARIMA-ANN models. After that, runoff predictions were performed for each model. As comparison criteria, Root Mean Square Error (RMSE), Kling Gupta Efficiency (KGE), and Nash Sutcliffe Efficiency (NSE) were taken into consideration. As a result, it was obtained that WARIMA and WARIMA-ANN models performed better than the ARIMA and ANN models, particularly. In addition, it was seen that wavelet transformation improved the performance of ARIMA and ARIMA-ANN models, obviously.
|Thanks||The authors are grateful to General Directorate of State Hydraulic Works for providing the data. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors declare that they have no conflict of interest.|
: February 19, 2021
|APA||Sezen, C , Partal, T . (2021). Aylık Akış Tahmini için Stokastik, Yapay Sinir Ağı ve Dalgacık Bazlı Modellerin Kullanımı . Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi , 8 (1) , 228-240 . DOI: 10.35193/bseufbd.878624|