A Hybrid Wavelet and Neuro-Fuzzy Model for Forecasting the Monthly Streamflow Data
Küçük Resim Yok
Tarih
2014
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SPRINGER
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Researchers have studied to forecast the streamflow in order to develop the water usage policy. They have used not only traditional methods, but also computer aided methods. Some black-box models, like Adaptive Neuro Fuzzy Inference Systems (ANFIS), became very popular for the hydrologic engineering, because of their rapidity and less variation requirements. Wavelet Transform has become a useful tool for the analysis of the variations in time series. In this study, a hybrid model, Wavelet-Neuro Fuzzy (WNF), has been used to forecast the streamflow data of 5 Flow Observation Stations (FOS), which belong to Sakarya Basin in Turkey. In order to evaluate the accuracy performance of the model, Auto Regressive Integrated Moving Average (ARIMA) model has been used with the same data sets. The comparison has been made by Root Mean Squared Errors (RMSE) of the models. Results showed that hybrid WNF model forecasts the streamflow more accurately than ARIMA model.
Açıklama
Anahtar Kelimeler
Streamflow modeling, Wavelet, Anfis, ARIMA
Kaynak
WATER RESOURCES MANAGEMENT
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
28
Sayı
2
