Open AccessComparative study among different neural net learning algorithms applied to rainfall time series
Author(s) -
Chattopadhyay Surajit,
Chattopadhyay Goutami
Publication year - 2008
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.71
Subject(s) - backpropagation , artificial neural network , series (stratigraphy) , conjugate gradient method , computer science , time series , machine learning , algorithm , gradient descent , artificial intelligence , rprop , recurrent neural network , types of artificial neural networks , geology , paleontology
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