DocumentCode
1955753
Title
Notice of Retraction
Comparative study of Jorden and Elman model of neural network for short term flood forecasting
Author
Deshmukh, R.P. ; Ghatol, A.A.
Author_Institution
Indian Inst. of Technol., Mumbai, India
Volume
9
fYear
2010
fDate
9-11 July 2010
Firstpage
400
Lastpage
404
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates a temporal approach by applying Jordan and Elman network for rainfall-runoff modeling for the upper area of Wardha River in India. The model is developed by processing online data over time using recurrent connections. Methodologies and techniques of the two models are presented in this paper and a comparison of the short term runoff prediction results between them is also conducted. The prediction results of the Jordan network indicate a satisfactory performance in the three hours ahead of time prediction. The conclusions also indicate that the Jordan network is more versatile than Elman model and can be considered as an alternate and practical tool for predicting short term flood flow.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates a temporal approach by applying Jordan and Elman network for rainfall-runoff modeling for the upper area of Wardha River in India. The model is developed by processing online data over time using recurrent connections. Methodologies and techniques of the two models are presented in this paper and a comparison of the short term runoff prediction results between them is also conducted. The prediction results of the Jordan network indicate a satisfactory performance in the three hours ahead of time prediction. The conclusions also indicate that the Jordan network is more versatile than Elman model and can be considered as an alternate and practical tool for predicting short term flood flow.
Keywords
disasters; floods; neural nets; rain; rivers; weather forecasting; Elman model; Jordan network; Wardha River; artificial neural networks; flood forecasting; hydrologic problem; online data processing; rainfall runoff modeling; time prediction; Artificial neural networks; Computational modeling; Predictive models; Artificial neural network; Forecasting; Models; Rainfall; Runoff;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5564917
Filename
5564917
Link To Document