Establishment of an Improved Elman Neural Network Model for Predicting the Corrosion Rate of 3C Steel in Marine Environment and Analysis of the Factors Affecting Model Accuracy

3C steel is a kind of steel commonly used in marine engineering, which will suffer different degrees of corrosion in the marine environment. In the marine environment, there is a complex nonlinear relationship between the corrosion rate and seawater environmental parameters. Based on the experimental data of corrosion rates of 3C steel in different seawater environments, an improved Elman neural network model was established by using the whale optimization algorithm. The corrosion rates of 3C steel in different seawater environments were predicted, and the influences of the number of hidden layer nodes, the population sizes, and the number of iterations on the prediction results of the improved model were analyzed. The results show that the prediction results of the improved Elman neural network model are in good agreement with the experimental results; the average relative error and the root mean square error are 1.0564% and 0.195, respectively. With the increase in the number of hidden layer nodes and the population sizes, the average relative errors of the predicted results decrease first and then increase. With the increase in the number of iterations, the average relative errors of the predicted results decrease first, then increase, and finally decrease. The improved Elman neural network model has the advantage of high prediction accuracy and can be applied to the prediction of the corrosion rate of 3C steel in the marine environment.