Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach Article Swipe
Jian Chen
,
Ming Yan
,
Muhammad Rabea Hanzla Qureshi
,
Keke Geng
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1049/sil2.12164
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1049/sil2.12164
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/sil2.12164
- OA Status
- gold
- Cited By
- 6
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4295346013
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4295346013Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1049/sil2.12164Digital Object Identifier
- Title
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Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approachWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-09-13Full publication date if available
- Authors
-
Jian Chen, Ming Yan, Muhammad Rabea Hanzla Qureshi, Keke GengList of authors in order
- Landing page
-
https://doi.org/10.1049/sil2.12164Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1049/sil2.12164Direct OA link when available
- Concepts
-
Visibility, Computer science, Wind speed, Principal component analysis, Artificial neural network, Weather forecasting, Artificial intelligence, Recurrent neural network, Meteorology, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 3, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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29Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.was | 39, 52, 67, 178 |
| abstract_inverted_index.With | 148 |
| abstract_inverted_index.aids | 164 |
| abstract_inverted_index.both | 14 |
| abstract_inverted_index.data | 27, 81, 109 |
| abstract_inverted_index.days | 8 |
| abstract_inverted_index.deep | 33 |
| abstract_inverted_index.from | 29, 58 |
| abstract_inverted_index.only | 133 |
| abstract_inverted_index.road | 15 |
| abstract_inverted_index.sets | 78 |
| abstract_inverted_index.some | 113 |
| abstract_inverted_index.that | 121 |
| abstract_inverted_index.this | 42 |
| abstract_inverted_index.used | 53, 68, 92 |
| abstract_inverted_index.well | 111 |
| abstract_inverted_index.were | 91 |
| abstract_inverted_index.wind | 89 |
| abstract_inverted_index.with | 105, 163 |
| abstract_inverted_index.(RNN) | 37 |
| abstract_inverted_index.After | 75 |
| abstract_inverted_index.Based | 21 |
| abstract_inverted_index.Then, | 61 |
| abstract_inverted_index.based | 132 |
| abstract_inverted_index.data, | 138, 152 |
| abstract_inverted_index.data. | 60 |
| abstract_inverted_index.foggy | 7 |
| abstract_inverted_index.found | 120 |
| abstract_inverted_index.great | 11 |
| abstract_inverted_index.image | 83, 137 |
| abstract_inverted_index.makes | 125 |
| abstract_inverted_index.model | 38, 124, 157 |
| abstract_inverted_index.other | 114 |
| abstract_inverted_index.study | 43 |
| abstract_inverted_index.that, | 76 |
| abstract_inverted_index.train | 96 |
| abstract_inverted_index.video | 26, 59 |
| abstract_inverted_index.First, | 48 |
| abstract_inverted_index.Neural | 35 |
| abstract_inverted_index.actual | 107 |
| abstract_inverted_index.factor | 188 |
| abstract_inverted_index.grades | 145 |
| abstract_inverted_index.higher | 141 |
| abstract_inverted_index.inputs | 94 |
| abstract_inverted_index.method | 66 |
| abstract_inverted_index.model. | 99 |
| abstract_inverted_index.models | 131 |
| abstract_inverted_index.night, | 183 |
| abstract_inverted_index.reduce | 70 |
| abstract_inverted_index.sample | 80 |
| abstract_inverted_index.speed, | 90 |
| abstract_inverted_index.18.78%. | 161 |
| abstract_inverted_index.Fourier | 50 |
| abstract_inverted_index.Network | 36 |
| abstract_inverted_index.extract | 55 |
| abstract_inverted_index.factors | 173 |
| abstract_inverted_index.feature | 56 |
| abstract_inverted_index.include | 82 |
| abstract_inverted_index.predict | 45 |
| abstract_inverted_index.results | 104 |
| abstract_inverted_index.safety. | 20 |
| abstract_inverted_index.traffic | 16 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Analysis | 65 |
| abstract_inverted_index.Besides, | 162 |
| abstract_inverted_index.accuracy | 142, 154 |
| abstract_inverted_index.airport, | 31 |
| abstract_inverted_index.dominant | 187 |
| abstract_inverted_index.improved | 159 |
| abstract_inverted_index.methods, | 116 |
| abstract_inverted_index.proposed | 123 |
| abstract_inverted_index.research | 2 |
| abstract_inverted_index.Component | 64 |
| abstract_inverted_index.Principal | 63 |
| abstract_inverted_index.Recurrent | 34 |
| abstract_inverted_index.Transform | 51 |
| abstract_inverted_index.affecting | 189 |
| abstract_inverted_index.analysed, | 179 |
| abstract_inverted_index.analysis, | 167 |
| abstract_inverted_index.collected | 28 |
| abstract_inverted_index.comparing | 101 |
| abstract_inverted_index.detection | 5 |
| abstract_inverted_index.different | 144 |
| abstract_inverted_index.dimension | 72 |
| abstract_inverted_index.features, | 84 |
| abstract_inverted_index.features. | 74 |
| abstract_inverted_index.influence | 169 |
| abstract_inverted_index.predicted | 103, 176 |
| abstract_inverted_index.pressure, | 86 |
| abstract_inverted_index.transport | 19 |
| abstract_inverted_index.variables | 57 |
| abstract_inverted_index.deficiency | 129 |
| abstract_inverted_index.visibility | 4, 108, 177 |
| abstract_inverted_index.considering | 149 |
| abstract_inverted_index.correlation | 166 |
| abstract_inverted_index.established | 40 |
| abstract_inverted_index.temperature | 87, 184 |
| abstract_inverted_index.visibility. | 47, 147, 190 |
| abstract_inverted_index.significance | 12 |
| abstract_inverted_index.meteorological | 24, 135, 151, 172 |
| abstract_inverted_index.state‐of‐the‐art | 115 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.69573209 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |