Notice of RetractionIntelligent modeling of urban water supply prediction

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.

To reduce energy and water, water supply company need estimate future water consumption according to the record of daily water supply, and best arrange future production planning and control, water consumption is uncertainty and is strong non-linear time series, water consumption prediction estimation is more concerned by academics, it is predicted through various methods, multiple regression analysis and gray forecast are the most common method at present, these methods are difficult to give a satisfactory result according to the characteristic of nonlinear and time varying. In accordance with the disadvantage above methods, a new intelligent model is presented to predict accurately water consumption of a city based on optimal common machine learning algorithm- support vector machine in this paper. Complex and strong nonlinear water consumption was simulated by network design and conformation of support vector machine learning algorithm and the optimized support vector machine parameters were selected by the method of network searching and cross validation according to existing data. Compared the errors with output value of the optimized model and output value from grey model, support vector machine whose parameter was optimized with cross validation had excellent ability of nonlinear modeling and generalization. It provides a simple and feasible intelligent approach for water consumption prediction.