Prediction of Oil Pipeline Internal Corrosion Rate Based on FOA-SVM model

Using multivariate statistical analysis method, a new prediction model for pipeline internal corrosion rate was put forward on the basis of support vector machine(SVM) by collecting the measured data of internal corrosion rates of the oil pipeline. Fruit flies optimization algorithm (FOA) was used to optimize the training of prediction model and establish the FOA-SVM forecasting model. The forecast result was checked by using measured sample data. Considering that the integrated variance was 1.397×10^-3 and the mean deviation was 0.037 4, the FOA-SVM prediction model had better stability and higher precision compared with the grey combinational model. However, due to the longer training time of the FOA-SVM prediction model, further study is still needed to improve the model prediction efficiency.