Notice of RetractionGas bearing capacity forecasting method based on ant colony optimization and support vector regression

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 recent years, new energy´s exploitation is becoming more and more important, one type of the new energy is gas bearing in the coal layers. Forecasting for coal-seam gas content is a complicated non-linear forecasting problem, which is difficult to solve. A prediction model of coal-seam gas capacity based on support vector regression and ant colony optimization is presented in this paper. As there is the relationship between the operational parameters of support vector regression and support vector regression prediction model, ant colony optimization is applied to select the operational parameters of support vector regression. The experimental results indicate that the forecasting precision of support vector regression and ant colony optimization is better than that of BP neural network. It is indicated that the prediction model meets the requirement of coal-seam gas content prediction.