Wavelet and neuro-fuzzy conjunction model for streamflow forecasting

Kişi Ö., Partal T.

Hydrology Research, cilt.42, sa.6, ss.447-456, 2011 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 42 Sayı: 6
  • Basım Tarihi: 2011
  • Doi Numarası: 10.2166/nh.2011.048
  • Dergi Adı: Hydrology Research
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.447-456
  • Anahtar Kelimeler: Discrete wavelet transform, Forecast, Neuro-fuzzy, Streamflow
  • Ondokuz Mayıs Üniversitesi Adresli: Hayır

Özet

In this study the wavelet-neuro-fuzzy model, which combines the wavelet transform and the neurofuzzy technique, has been employed to forecast monthly streamflows. The observed monthly streamflow data are decomposed into some sub-series (components) by discrete wavelet transform and then appropriate sub-series are used as inputs to the neuro-fuzzy models for forecasting monthly streamflows. The data from two stations, Durucasu and Tanir, in Turkey are used as case studies. The wavelet-neuro-fuzzy forecasts are compared with those of the single neuro-fuzzy models. Comparison results indicate that the wavelet-neuro-fuzzy model is superior to the classical neuro-fuzzy method especially for the peak values. For the Durucasu and Tanir stations, it was found that the wavelet-neuro-fuzzy models are superior in forecasting monthly streamflows than the optimal neuro-fuzzy models. © IWA Publishing 2011.