Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach

Abstract

The research of visibility detection in foggy days is of great significance to both road traffic and air transport safety. Based on the meteorological and video data collected from an airport, a deep Recurrent Neural Network (RNN) model was established in this study to predict the visibility. First, the Fourier Transform was used to extract feature variables from video data. Then, the Principal Component Analysis method was used to reduce the dimension of features. After that, 462 sets of sample data include image features, air pressure, temperature and wind speed, were used as inputs to train the RNN model. By comparing the predicted results with the actual visibility data as well as some other stateof-the-art methods, it can be found that the proposed model makes up for the deficiency of models based only on meteorological or image data, and has higher accuracy in different grades of visibility. With considering the meteorological data, the accuracy of RNN model is improved by 18.78%. Besides, with aids of correlation analysis, the influence of the meteorological factors on the predicted visibility was analysed, for fog at night, temperature is the dominant factor affecting visibility.