Comparison of Wavelet Based Hybrid Models for Daily Evapotranspiration Estimation using Meteorological Data

Partal T.

KSCE JOURNAL OF CIVIL ENGINEERING, cilt.20, sa.5, ss.2050-2058, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 20 Sayı: 5
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1007/s12205-015-0556-0
  • Dergi Adı: KSCE JOURNAL OF CIVIL ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2050-2058
  • Anahtar Kelimeler: wavelet transformation, radial basis neural network, feed forward neural network, multi linear regression, evapotranspiration, estimating, SUSPENDED SEDIMENT DATA, NEURAL-NETWORK
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

This paper investigates the comparative performance of wavelet based radial basis networks and multi linear regression in daily reference evapotranspiration estimation. The meteorological data (air temperature, solar radiation, wind speed, relative humidity) from two stations in the United States was evaluated for estimating models. The wavelet based radial basis network combines wavelet transformation and radial basis neural network, while the wavelet based regression model combines wavelet transformation and multi linear regression. The results show that the wavelet transformation has significantly positive effects on modeling performance. The wavelet based radial basis network provided the best performance evaluation criteria.