Development and comparative analysis of machine learning algorithms for predictive atmospheric corrosion modeling

This work represents a significant advancement in the predictive modeling of atmospheric corrosion. It highlights the invaluable role of machine learning in this field. By integrating varied data sets and applying sophisticated machine learning techniques, it has established a foundation for ongoing research and the practical application of corrosion management strategies. The exceptional performance of ensemble methods, like random forests, signals their potential to improve prediction capabilities and guide more effective corrosion prevention measures.