DocumentCode
2650352
Title
Notice of Retraction
Forecasting of precipitation by RBF neural network and particle swarm optimization
Author
Fu Fei ; Zhang Jian ; Zhou bao qi
Author_Institution
Fac. of Archit., Southwest Jiaotong Univ., Chengdu, China
Volume
7
fYear
2010
fDate
16-18 April 2010
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.
Prediction of precipitation is very significant for dispatching and control of water in river to manage river and flood. RBF neural network trained with particle swarm optimization is presented to predict precipitation in the study. RBF neural network has strong nonlinear prediction ability, which has considerable applications. In the study, RBF neural network trained with particle swarm optimization is presented to predict precipitation. In RBF neural network, forecasting accuracy is affected by its operational parameters. Particle swarm optimization technique is used as a training phase of RBF neural network to determine its optimal parameters. A certain river irrigated area precipitation data from 1970 to 2002 are adopted to evaluate the performance of the proposed method. The testing results show that the proposed model is superior to BP neural network.
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.
Prediction of precipitation is very significant for dispatching and control of water in river to manage river and flood. RBF neural network trained with particle swarm optimization is presented to predict precipitation in the study. RBF neural network has strong nonlinear prediction ability, which has considerable applications. In the study, RBF neural network trained with particle swarm optimization is presented to predict precipitation. In RBF neural network, forecasting accuracy is affected by its operational parameters. Particle swarm optimization technique is used as a training phase of RBF neural network to determine its optimal parameters. A certain river irrigated area precipitation data from 1970 to 2002 are adopted to evaluate the performance of the proposed method. The testing results show that the proposed model is superior to BP neural network.
Keywords
atmospheric precipitation; backpropagation; particle swarm optimisation; radial basis function networks; BP neural network; RBF neural network; flood; nonlinear prediction ability; particle swarm optimization; precipitation forecasting; river irrigated area precipitation data; river water control; Birds; Dispatching; Management training; Neural networks; Particle swarm optimization; Performance analysis; Predictive models; Rivers; Testing; forecasting; management of river and flood; neural network; optimization; particle swarm; precipitation;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5485488
Filename
5485488
Link To Document