DocumentCode
3099160
Title
Notice of Retraction
The Model of Phase Space Reconstruction and Neural Network about the Natural Disaster Losing Forecasting
Author
Lianhai Cao ; Zhiping Li ; Nanxiang Chen
Author_Institution
North China Univ. of Water Conservancy & Hydroelectric Power, Zhengzhou, China
fYear
2010
fDate
18-20 June 2010
Firstpage
1
Lastpage
4
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.
Introducing chaos theory into the floodwater disaster resources field, the forecasting model for the inundated area of flood disaster was brought forward integrating reconstruction of phase space and neural network. One dimension inundated area series is developed many dimension inundated area series with reconstruction of phase space, and multi- dimension series include the ergodic information, so the more rich information can be excavated for ANN training; and the non-linear problem better can be solved by the neural network, as a result, forecasting can be more fitting for practice. The example shows the model has highly fitting accuracy and forecasting precision.
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.
Introducing chaos theory into the floodwater disaster resources field, the forecasting model for the inundated area of flood disaster was brought forward integrating reconstruction of phase space and neural network. One dimension inundated area series is developed many dimension inundated area series with reconstruction of phase space, and multi- dimension series include the ergodic information, so the more rich information can be excavated for ANN training; and the non-linear problem better can be solved by the neural network, as a result, forecasting can be more fitting for practice. The example shows the model has highly fitting accuracy and forecasting precision.
Keywords
chaos; disasters; floods; geophysics computing; learning (artificial intelligence); neural nets; ANN; chaos theory; floodwater disaster resources field; natural disaster losing forecasting; neural network; phase space reconstruction; Artificial neural networks; Chaos; Economic forecasting; Home appliances; Image reconstruction; Neural networks; Predictive models; Typhoons; Water conservation; Water resources;
fLanguage
English
Publisher
ieee
Conference_Titel
Bioinformatics and Biomedical Engineering (iCBBE), 2010 4th International Conference on
Conference_Location
Chengdu
ISSN
2151-7614
Print_ISBN
978-1-4244-4712-1
Type
conf
DOI
10.1109/ICBBE.2010.5515415
Filename
5515415
Link To Document