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

Industrial content and infrastructure are in constant danger from atmospheric corrosion, which affects economies globally. However, there is a lack of a consistent set of comprehensive data that completely surrounds the range of this problem in diverse climate and locations. The purpose of the research is to evaluate the factors that contribute to atmospheric corrosion and its diverse effects on materials in various environments.

By creating a comprehensive dataset by collecting and standardizing corrosion data from diverse environments and geographic regions and initially analyzing the data, it helped indicate the main parameters affecting corrosion. This guided the selection of future features for further modeling. Several machine learning algorithms were tested, such as linear regression, decisions tree, neural network, and, most especially, promising methods, for their corrosion rate prediction capabilities. These models were assessed based on their prediction's accuracy, and computational efficiency, with special attention to refining their performance through detailed feature engineering and hyperparameter adjustment.

Upon evaluating the performance of conventional predictive models, the research indicated that the machine learning approaches, especially with random forests methods of dress, were excellent in predicting corrosion rates, significantly improved upon these capabilities. By analyzing various machine learning approaches, it became clear that it was important to enhance their accuracy by selecting the best features and customizing them.

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.