DocumentCode
2645988
Title
Notice of Retraction
Application of genetic algorithm-RBF neural network in water environment risk prediction
Author
Liu Changbing ; Huang Wei
Author_Institution
Tianjin Res. Inst. of Water Transp. Eng., Tianjin, 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.
Water environment risk prediction is significant to improve utilization efficiency of water resource. In the study, genetic algorithm-RBF neural network is applied to water environment risk prediction, where RBF neural network has very good nolinear forecasting ability, and genetic algorithm is used to select the parameters of RBF neural network. The four indices of water environment include gross amount of water resources, per capita water resources, total volume of water consumption and total amount of discharge for wastewaters. The future water environment risk situation can be gained by forecasting for the four indices. The water environment data from 2000 to 2006 in China are adopted as our experimental data. The experimental results indicate that the forecasting results of the four indices of water environment of GA-RBFNN are better than grey model.
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.
Water environment risk prediction is significant to improve utilization efficiency of water resource. In the study, genetic algorithm-RBF neural network is applied to water environment risk prediction, where RBF neural network has very good nolinear forecasting ability, and genetic algorithm is used to select the parameters of RBF neural network. The four indices of water environment include gross amount of water resources, per capita water resources, total volume of water consumption and total amount of discharge for wastewaters. The future water environment risk situation can be gained by forecasting for the four indices. The water environment data from 2000 to 2006 in China are adopted as our experimental data. The experimental results indicate that the forecasting results of the four indices of water environment of GA-RBFNN are better than grey model.
Keywords
environmental science computing; genetic algorithms; neural nets; radial basis function networks; risk management; water resources; genetic algorithm-RBF neural network; nolinear forecasting ability; utilization efficiency; water environment risk prediction; water resource; Biological cells; Feedforward neural networks; Genetic algorithms; Genetic engineering; Genetic mutations; Neural networks; Predictive models; Training data; Wastewater; Water resources; artifical neural network; grey prediction model; nolinear forecasting; water environment;
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.2010.5485248
Filename
5485248
Link To Document