Visibility classification and influencing-factors analysis of airport: A deep learning approach

This paper discusses the visibility classification and influencing-factor analysis of an airport, which were carried out through a data-driven deep learning approach and multiple nonlinear regression analysis. First, a deep integrated model for airport visibility classification was developed, and the relationship between runway visual range (RVR) and surface meteorological elements for different reasons was analyzed. To extract more low-level features from nighttime images, the original images were transformed into pseudo-color images, and the image pairs were input into the classification model. The proposed deep integrated model combined two popular convolutional neural network (CNN) models: VGG-16 and Xception. The results showed that the accuracy of the proposed model was 87.64%, with the F1-score being 88.58%; it achieved the best performance among other well-known experimental CNNs. Afterward, the relationships between RVR and meteorological optical range (MOR), source light intensity (I), and background light (BL) were analyzed, and the results showed that RVR increased with increasing I under the same MOR and BL conditions; however, it decreased with increasing log (BL) values under the same I and MOR conditions. RVR increased as MOR increased when other conditions remained unchanged. Subsequently, the multivariate regression analysis was conducted to fit the relationship between MOR and surface meteorological elements, and the results showed that the cubic curve was the best-fit curve with R-square values of 0.937 and 0.860 in December 2019 and March 2020, respectively. Finally, combined with the analysis of the factors influencing the RVR, a significant negative correlation was observed with relative humidity, along with a significant positive correlation with temperature and a positive correlation with average wind speed in the winter of 2019 and spring of 2020. However, the pressure factor showed the opposite correlation, possibly because the arrival of cold air in winter increases the ground pressure, which is conducive for the dissipation of pollutants. Meanwhile, the increase in temperature and decrease in pressure in spring increases the chance of disturbances in the atmosphere, thereby increasing the likelihood of adverse visibility factors. ◆ Pseudo-color images were found beneficial for improving the accuracy of visibility identification. ◆ An integrated model combined VGG-16 and Xception achieved the best performance compared to that of other popular models. ◆ The relationship between RVR and MOR, I, and BL was found. ◆ The multivariate regression analysis between MOR and surface meteorological elements in different seasons was conducted. ◆ There is a correlational relationship between RVR and surface meteorological elements.