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

Turgay Partal*, H. Kerem Cigizoglu, Ercan Kahya

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1317-1329
Number of pages13
JournalStochastic Environmental Research and Risk Assessment
Volume29
Issue number5
DOIs
Publication statusPublished - 10 Jul 2015

Bibliographical note

Publisher Copyright:
© 2015, Springer-Verlag Berlin Heidelberg.

Keywords

  • Artificial neural networks
  • Estimation
  • Linear regression
  • Precipitation
  • Wavelet transformation

Fingerprint

Dive into the research topics of 'Daily precipitation predictions using three different wavelet neural network algorithms by meteorological data'. Together they form a unique fingerprint.

Cite this