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Estimation and prediction of fog day-based visibility based on convolutional neural network

Visibility prediction is a concern issue in the field of public transportation, which is related to the normal operation of flights and the safe travel of vehicles. Therefore, reasonable prediction of visibility is very important to improve the efficiency and safety of public transportation. This paper mainly combines data and video, and images are quantified for quantitative analysis of large fog evolution trends. Further, the mathematical model is established to study the visibility prediction problem that is concerned with the current traffic, aviation field, and proposes targeted recommendations for the current predictive means. Preprocessing the sample data, eliminates the wild value, performs interpolation processing on the missing position, regression analysis of the image, obtains the regression model of image visibility changes, performs visibility prediction, select accuracy, adaptive ability, good depth integration Convolutional Neural Network (CNN) is an algorithm for learning processing on the image. In the model establishment, the three image processing modes (Fourier change algorithm, spectral filtering, original pictures) are established, and the hidden feature, depth integrated volume is established by the three image processing modes (Fourier change algorithm, spectral filtering, original pictures). Total Neural Network (CNN) simultaneously learns three images of the input and generates a classification output, and finally the categorized visual network model.