DocumentCode
249865
Title
Notice of Violation of IEEE Publication Principles
Comparative Study among Different Neural Net Learning Algorithms Applied to Rainfall Predication
Author
Kulkarni, Santosh ; Mushrif, Milind
Author_Institution
Dept. of Electron. & Telecommun., MIT Acad. of Eng., Pune, India
fYear
2014
fDate
9-11 Jan. 2014
Firstpage
209
Lastpage
216
Abstract
Notice of Violation of IEEE Publication Principles
???Comparative Study Among Different Neural Net Learning Algorithms Applied to Rainfall Predication???
by Smita Kulkarni and Milind Mushrif
in the Proceedings of the International Conference on Electronic Systems, Signal Processing and Computing Technologies??? January 2014, pp. 209-216
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.
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.
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:
???Comparative Study Amoung Different Neural Net Learning Algorithms Applied to Rainfall Time Series???
by Surajit Chattopadhyay and Goutami Chattopadhyay
in Meteorological Applications, 15, Wiley Interscience, April 2008, pp. 273-280
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.
???Comparative Study Among Different Neural Net Learning Algorithms Applied to Rainfall Predication???
by Smita Kulkarni and Milind Mushrif
in the Proceedings of the International Conference on Electronic Systems, Signal Processing and Computing Technologies??? January 2014, pp. 209-216
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.
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.
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:
???Comparative Study Amoung Different Neural Net Learning Algorithms Applied to Rainfall Time Series???
by Surajit Chattopadhyay and Goutami Chattopadhyay
in Meteorological Applications, 15, Wiley Interscience, April 2008, pp. 273-280
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.
Keywords
backpropagation; geophysics computing; monsoons; neural nets; rain; statistical analysis; time series; weather forecasting; CGD learning; India; LM learning; Levenberg-Marquardt learning; asymptotic regression; backpropagation neural network learning; conjugate gradient descent learning; momentum learning; rainfall predication; statistical methodology; summer-monsoon rainfall; time series forecasting; Artificial neural networks; Backpropagation; Correlation; Mathematical model; Predictive models; Time series analysis; multilayer perceptron; backpropagation learning; momentum; conjugate gradient descent; LevenbergMarquardt; asymptotic regression; monsoon rainfall;
fLanguage
English
Publisher
ieee
Conference_Titel
Electronic Systems, Signal Processing and Computing Technologies (ICESC), 2014 International Conference on
Conference_Location
Nagpur
Type
conf
DOI
10.1109/ICESC.2014.104
Filename
6745375
Link To Document