Abstract
Machine learning has been widely applied to exploring the key affecting factors for metal corrosion. However, there is a lack of appropriate algorithm for the metal corrosion in a broad region. In this paper, the atmospheric corrosion map in Hunan, China was constructed and optimized by field experiments and machine learning. Relative humidity, precipitation, SO2 concentrations, NO2 concentrations and PM10 index were the key affecting factors of atmospheric corrosion in the region. With the identification of key affecting factors, the relative error of the ordinary least square (OLS) algorithm was lower than that of the random forest (RF) and support vector regression (SVR) algorithms. The relationship between the key affecting factors and corrosion rate was obtained by the OLS algorithm. For the tested field sites, the optimized corrosion map was constructed through the OLS algorithm, with the relative error less than 6%.
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