Akademik Veri Yönetim Sistemi
Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, cilt.8, sa.1, ss.228-240, 2021 (Hakemli Dergi)
The development of various models to estimate hydrological variables, such as precipitation and runoff issignificant regarding handling the water-related problems in the future. This study investigates the performancesof 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 twostations of the Susurluk basin in Turkey. In this regard, first, the streamflow data were decomposed intocomponents by wavelet transformation for the WARIMA and WARIMA-ANN models. After that, runoffpredictions were performed for each model. As comparison criteria, Root Mean Square Error (RMSE), KlingGupta Efficiency (KGE), and Nash Sutcliffe Efficiency (NSE) were taken into consideration. As a result, it wasobtained 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 andARIMA-ANN models, obviously.