Neural networks adaptive wavelets for predictions of the northeastern Brazil monthly rainfall anomalies time series

Weigang Li; Leonardo Deane Sa; G. S.S. Prasad; A. G. Nowosad; Mauricio Bolzan; E. S. M. Chiang

doi:10.1117/12.235908

22 March 1996 Neural networks adaptive wavelets for predictions of the northeastern Brazil monthly rainfall anomalies time series

Weigang Li, Leonardo Deane Sa, G. S.S. Prasad, A. G. Nowosad, Mauricio Bolzan, E. S. M. Chiang

Author Affiliations +

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235908
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

Neural networks were used to predict the anomalies of the time series of monthly rainfall of the Northeastern Region of Brazil. The forecasts made using a feedforward network with backpropagation algorithm from the original data were not satisfactory. We have therefore tried to combine two advanced methods, Wavelet Transform and Neural networks. Three more types of neural networks were used. The selected neural networks include the Time Delay Neural Networks (TDNN), Radial Basis Functions network and Neural Network Adaptive Wavelet. All networks were implemented in neural network simulator SNNS. The Neural Network Adaptive Wavelet was implemented by changing the standard sigmoidal nonlinearities to wavelet nonlinearities in the neurons. We compare the results obtained with unfiltered and filtered data. Using data obtained by filtering the wavelet transform coefficients significantly improved the results for all networks. The combination of TDNN with wavelet filtered data gave the best results.

Citation Download Citation

Weigang Li, Leonardo Deane Sa, G. S.S. Prasad, A. G. Nowosad, Mauricio Bolzan, and E. S. M. Chiang "Neural networks adaptive wavelets for predictions of the northeastern Brazil monthly rainfall anomalies time series", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235908

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available