Comparative Study among Different Neural Net Learning Algorithms Applied to Rainfall Predication

摘要

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.