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 visibilit...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | IET Signal Processing |
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| Online Access: | https://doi.org/10.1049/sil2.12164 |
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| author | Jian Chen Ming Yan Muhammad Rabea Hanzla Qureshi Keke Geng |
| author_facet | Jian Chen Ming Yan Muhammad Rabea Hanzla Qureshi Keke Geng |
| author_sort | Jian Chen |
| collection | DOAJ |
| description | 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 state‐of‐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. |
| format | Article |
| id | doaj-art-82c4d33020f8499cb8b88a5b12ee55dd |
| institution | Kabale University |
| issn | 1751-9675 1751-9683 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-82c4d33020f8499cb8b88a5b12ee55dd2025-02-03T06:47:27ZengWileyIET Signal Processing1751-96751751-96832023-01-01171n/an/a10.1049/sil2.12164Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approachJian Chen0Ming Yan1Muhammad Rabea Hanzla Qureshi2Keke Geng3School of Mechanical Engineering Yangzhou University Yangzhou ChinaSchool of Mechanical Engineering Yangzhou University Yangzhou ChinaSchool of Mechanical Engineering Yangzhou University Yangzhou ChinaSchool of Mechanical Engineering Southeast University Nanjing ChinaAbstract 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 state‐of‐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.https://doi.org/10.1049/sil2.12164correlation analysisdata dimension reductionfourier transformprincipal component analysis (PCA)Recurrent Neural Network (RNN) model |
| spellingShingle | Jian Chen Ming Yan Muhammad Rabea Hanzla Qureshi Keke Geng Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach IET Signal Processing correlation analysis data dimension reduction fourier transform principal component analysis (PCA) Recurrent Neural Network (RNN) model |
| title | Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach |
| title_full | Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach |
| title_fullStr | Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach |
| title_full_unstemmed | Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach |
| title_short | Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach |
| title_sort | estimating the visibility in foggy weather based on meteorological and video data a recurrent neural network approach |
| topic | correlation analysis data dimension reduction fourier transform principal component analysis (PCA) Recurrent Neural Network (RNN) model |
| url | https://doi.org/10.1049/sil2.12164 |
| work_keys_str_mv | AT jianchen estimatingthevisibilityinfoggyweatherbasedonmeteorologicalandvideodataarecurrentneuralnetworkapproach AT mingyan estimatingthevisibilityinfoggyweatherbasedonmeteorologicalandvideodataarecurrentneuralnetworkapproach AT muhammadrabeahanzlaqureshi estimatingthevisibilityinfoggyweatherbasedonmeteorologicalandvideodataarecurrentneuralnetworkapproach AT kekegeng estimatingthevisibilityinfoggyweatherbasedonmeteorologicalandvideodataarecurrentneuralnetworkapproach |