Application of APSO-BPNN Model in Prediction of Iron Material Corrosion Rate in Coastal Environment

Aiming at the problem of corrosion rate prediction of iron materials in coastal complex environment, an adaptive particle swarm optimization (APSO) algorithm was used to optimize the weights and thresholds in back propagation neural network (BPNN), and an APSO-BPNN model was constructed to improve the accuracy of corrosion rate prediction of iron materials in coastal environment. Based on the exposure experimental data, the predictive effects of APSO-BPNN model were compared with those of traditional BPNN model. The results showed that the APSO-BPNN model enhanced the determination coefficient R² by 23.65% on the training set. Its R² on the test set reached 0.925 8, and the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) decreased to 11.55, 22.26 % and 14.43, respectively.