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

Area of Science:

• Materials Science
• Environmental Science
• Data Science

Background:

• Atmospheric corrosion poses a global economic threat to industrial infrastructure.
• A lack of comprehensive data hinders understanding of corrosion across diverse climates and locations.
• Research aims to evaluate factors influencing atmospheric corrosion and its material effects.

Purpose of the Study:

• To develop a comprehensive dataset of atmospheric corrosion.
• To identify key parameters influencing corrosion rates.
• To evaluate machine learning algorithms for corrosion prediction.

Main Methods:

• Collected and standardized corrosion data from various environments and regions.
• Applied machine learning algorithms including linear regression, decision trees, and neural networks.
• Utilized feature engineering and hyperparameter tuning to optimize model performance.

Main Results:

• Machine learning models, especially random forests, demonstrated high accuracy in predicting corrosion rates.
• Feature selection and customization significantly enhanced model predictive capabilities.
• Ensemble methods showed superior performance compared to conventional models.

Conclusions:

• Machine learning, particularly ensemble methods like random forests, offers significant advancements in atmospheric corrosion prediction.
• This study establishes a foundation for enhanced corrosion management and prevention strategies.
• Integrating diverse datasets and advanced ML techniques is crucial for future research.