Forecast model for inner corrosion rate of oil pipeline based on PSO‐SVM

In order to improve the prediction precision of inner corrosion rate of oil pipeline, this paper proposes a novel forecast model, which combines the superior regression performance of a support vector machine and the global optimisation ability of particle swarm optimisation. The SVM regression model, with radial basis function (RBF) kernel, is established to facilitate the inner corrosion rate of oil pipeline and the global optimiser, PSO, is employed to optimise the SVM parameters needed in SVM regression. The proposed model can reduce the dimensionality of data space and preserve features of inner corrosion rate of oil pipeline. The proposed PSO‐SVM model, compared with BP neural network model, had higher accuracy and speed and the maximum error is 0.6%. Thus, it provides a new method for the forecast of inner corrosion rate of oil pipeline.