Notice of Violation of IEEE Publication Principles: Comparative Study among Different Neural Net Learning Algorithms Applied to Rainfall Predication

Abstract

Notice of Violation of IEEE Publication Principles<br><br>“Comparative Study Among Different Neural Net Learning Algorithms Applied to Rainfall Predication”<br>by Smita Kulkarni and Milind Mushrif <br>in the Proceedings of the International Conference on Electronic Systems, Signal Processing and Computing Technologies” January 2014, pp. 209-216<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.<br><br>This paper is a duplication of the original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br>Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:<br><br>“Comparative Study Amoung Different Neural Net Learning Algorithms Applied to Rainfall Time Series”<br>by Surajit Chattopadhyay and Goutami Chattopadhyay<br>in Meteorological Applications, 15, Wiley Interscience, April 2008, pp. 273-280<br><br> <br/> 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.