Research of Deep Learning-Based Visibility Prediction Model for Foggy Days in Airport

Abstract

In this paper, we estimate the visibility distance under heavy fog conditions based on the airport video image containing information about the varying process of fog and the observed meteorological data. This article uses data from 00:00:16 to 11:47:48 on March 13, 2020, for an airport. By establishing two regression models, the Multiple Linear Regression (MLR) model and the Multiple Polynomial Regression (MPR) model, to analyzing the relationship between visibility distance and meteorological observation data on the ground, and comparing and evaluating the two models. Experimentally, it was found that MPR models solve relational equations with higher precision, and if it used a higher order of MPR model, it has higher predicted precision. And we establish a deep learning model based on video visibility distance estimation from video data and meteorological observation data of an airport and evaluate the accuracy of the estimated visibility. The experimental results show that the percentage error between the predicted value and the true value is less than 0.25%, which achieves high predictive accuracy and has a very good prediction effect.