We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Daily precipitation predictions using three different wavelet neural network algorithms by meteorological data.
- Authors
Partal, Turgay; Cigizoglu, H.; Kahya, Ercan
- 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.
- Subjects
WAVELET transforms; METEOROLOGICAL precipitation measurement; ARTIFICIAL neural networks; PARAMETER estimation; REGRESSION analysis
- Publication
Stochastic Environmental Research & Risk Assessment, 2015, Vol 29, Issue 5, p1317
- ISSN
1436-3240
- Publication type
Article
- DOI
10.1007/s00477-015-1061-1