Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning

The atmospheric corrosion of carbon steel was monitored by a Fe/Cu type galvanic corrosion sensor for 34 days. Using a random forest (RF)-based machine learning approach, the impacts of relative humidity, temperature and rainfall were identified to be higher than those of airborne particles, sulfur dioxide, nitrogen dioxide, carbon monoxide and ozone on the initial atmospheric corrosion. The RF model demonstrated higher accuracy than artificial neural network (ANN) and support vector regression (SVR) models in predicting instantaneous atmospheric corrosion. The model accuracy can be further improved after taking into consideration of the significant effect of rust formation on the sensor.