Daily precipitation predictions using three different wavelet neural network algorithms by meteorological data

Partal T., Cigizoglu H. K., Kahya E.

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, cilt.29, sa.5, ss.1317-1329, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29 Sayı: 5
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1007/s00477-015-1061-1
  • Dergi Adı: STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1317-1329
  • Anahtar Kelimeler: Wavelet transformation, Artificial neural networks, Linear regression, Precipitation, Estimation, MONTHLY RAINFALL, MODEL
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

In this study, three different neural network algorithms (feed forward back propagation, FFBP; radial basis function; generalized regression neural network) and wavelet transformation were used for daily precipitation predictions. Different input combinations were tested for the precipitation estimation. As a result, the most appropriate neural network model was determined for each station. Also linear regression model performance is compared with the wavelet neural networks models. It was seen that the wavelet FFBP method provided the best performance evaluation criteria. The results indicate that coupling wavelet transforms with neural network can provide significant advantages for estimation process. In addition, global wavelet spectrum provides considerable information about the structure of the physical process to be modeled.