Comparative study among different neural net learning algorithms applied to rainfall time series
✍ Scribed by Surajit Chattopadhyay; Goutami Chattopadhyay
- Book ID
- 102511091
- Publisher
- John Wiley and Sons
- Year
- 2008
- Tongue
- English
- Weight
- 140 KB
- Volume
- 15
- Category
- Article
- ISSN
- 1350-4827
- DOI
- 10.1002/met.71
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✦ Synopsis
Abstract
The present article reports studies to identify a non‐linear methodology to forecast the time series of average summer‐monsoon rainfall over India. Three advanced backpropagation neural network learning rules namely, momentum learning, conjugate gradient descent (CGD) learning, and Levenberg–Marquardt (LM) learning, and a statistical methodology in the form of asymptotic regression are implemented for this purpose. Monsoon rainfall data pertaining to the years from 1871 to 1999 are explored. After a thorough skill comparison using statistical procedures the study reports the potential of CGD as a learning algorithm for the backpropagation neural network to predict the said time series. Copyright © 2008 Royal Meteorological Society