Notice of RetractionForecasting of precipitation by RBF neural network and particle swarm optimization

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.