Notice of RetractionApplication research on genetic neural network for water quality comprehensive evaluation

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.

In order to overcome the shortcomings of BP neural network, the golden section theory was used to get the reasonable number of Back Propagation (BP) neural network´s hidden nodes. By using Genetic Algorithm (GA) to optimize the initial weights and threshold value of BP neural network, the network converged quickly and the recognition precision was increased. The GA-BP neural network model was utilized to evaluate the water quality degree of 5 rivers in Xindu area of Chengdu, the evaluation results were compared with the results of single factor method and comprehensive pollution evaluation method. It indicated that the GA-BP neural network model has practicability, objectivity and generality in the application of water quality comprehensive evaluation.