Typhoon Trajectory Prediction by Three CNN+ Deep-Learning Approaches Article Swipe
Gang Lin
,
Yanchun Liang
,
Adriano Tavares
,
Carlos Lima
,
Dong Xia
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13193851
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13193851
The accuracy in predicting the typhoon track can be key to minimizing their frequent disastrous effects. This article aims to study the accuracy of typhoon trajectory prediction obtained by combining several algorithms based on current deep-learning techniques. The combination of a Convolutional Neural Network with Long Short-Term Memory (CNN+LSTM), Patch Time-Series Transformer (CNN+PatchTST) and Transformer (CNN+Transformer) were the models chosen for this work. These algorithms were tested on the best typhoon track data from the China Meteorological Administration (CMA), ERA5 data from the European Centre for Medium-Range Weather Forecasts (ECMWF), and structured meteorological data from the Zhuhai Meteorological Bureau (ZMB) as an extension of existing studies that were based only on public data sources. The experimental results were obtained by testing two complete years of data (2021 and 2022), as an alternative to the frequent selection of a small number of typhoons in several years. Using the R-squared metric, results were obtained as significant as CNN+LSTM (0.991), CNN+PatchTST (0.989) and CNN+Transformer (0.969). CNN+LSTM without ZMB data can only obtain 0.987, i.e., 0.004 less than 0.991. Overall, our findings indicate that appropriately augmenting data near land and ocean boundaries around the coast improves typhoon track prediction.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics13193851
- OA Status
- gold
- Cited By
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- References
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All OpenAlex metadata
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https://openalex.org/W4402990541Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/electronics13193851Digital Object Identifier
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Typhoon Trajectory Prediction by Three CNN+ Deep-Learning ApproachesWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-28Full publication date if available
- Authors
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Gang Lin, Yanchun Liang, Adriano Tavares, Carlos Lima, Dong XiaList of authors in order
- Landing page
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https://doi.org/10.3390/electronics13193851Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/electronics13193851Direct OA link when available
- Concepts
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Typhoon, Trajectory, Deep learning, Artificial intelligence, Computer science, Meteorology, Geography, Physics, AstronomyTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(ZMB) | 99 |
| abstract_inverted_index.0.004 | 171 |
| abstract_inverted_index.China | 75 |
| abstract_inverted_index.Patch | 49 |
| abstract_inverted_index.These | 63 |
| abstract_inverted_index.Using | 145 |
| abstract_inverted_index.based | 32, 108 |
| abstract_inverted_index.coast | 190 |
| abstract_inverted_index.i.e., | 170 |
| abstract_inverted_index.ocean | 186 |
| abstract_inverted_index.small | 138 |
| abstract_inverted_index.study | 20 |
| abstract_inverted_index.their | 12 |
| abstract_inverted_index.track | 6, 71, 193 |
| abstract_inverted_index.work. | 62 |
| abstract_inverted_index.years | 123 |
| abstract_inverted_index.(CMA), | 78 |
| abstract_inverted_index.0.987, | 169 |
| abstract_inverted_index.0.991. | 174 |
| abstract_inverted_index.2022), | 128 |
| abstract_inverted_index.Bureau | 98 |
| abstract_inverted_index.Centre | 84 |
| abstract_inverted_index.Memory | 47 |
| abstract_inverted_index.Neural | 42 |
| abstract_inverted_index.Zhuhai | 96 |
| abstract_inverted_index.around | 188 |
| abstract_inverted_index.chosen | 59 |
| abstract_inverted_index.models | 58 |
| abstract_inverted_index.number | 139 |
| abstract_inverted_index.obtain | 168 |
| abstract_inverted_index.public | 111 |
| abstract_inverted_index.tested | 66 |
| abstract_inverted_index.years. | 144 |
| abstract_inverted_index.(0.989) | 158 |
| abstract_inverted_index.Network | 43 |
| abstract_inverted_index.Weather | 87 |
| abstract_inverted_index.article | 17 |
| abstract_inverted_index.current | 34 |
| abstract_inverted_index.metric, | 148 |
| abstract_inverted_index.results | 116, 149 |
| abstract_inverted_index.several | 30, 143 |
| abstract_inverted_index.studies | 105 |
| abstract_inverted_index.testing | 120 |
| abstract_inverted_index.typhoon | 5, 24, 70, 192 |
| abstract_inverted_index.without | 163 |
| abstract_inverted_index.(0.969). | 161 |
| abstract_inverted_index.(0.991), | 156 |
| abstract_inverted_index.(ECMWF), | 89 |
| abstract_inverted_index.CNN+LSTM | 155, 162 |
| abstract_inverted_index.European | 83 |
| abstract_inverted_index.Overall, | 175 |
| abstract_inverted_index.accuracy | 1, 22 |
| abstract_inverted_index.complete | 122 |
| abstract_inverted_index.effects. | 15 |
| abstract_inverted_index.existing | 104 |
| abstract_inverted_index.findings | 177 |
| abstract_inverted_index.frequent | 13, 134 |
| abstract_inverted_index.improves | 191 |
| abstract_inverted_index.indicate | 178 |
| abstract_inverted_index.obtained | 27, 118, 151 |
| abstract_inverted_index.sources. | 113 |
| abstract_inverted_index.typhoons | 141 |
| abstract_inverted_index.Forecasts | 88 |
| abstract_inverted_index.R-squared | 147 |
| abstract_inverted_index.combining | 29 |
| abstract_inverted_index.extension | 102 |
| abstract_inverted_index.selection | 135 |
| abstract_inverted_index.Short-Term | 46 |
| abstract_inverted_index.algorithms | 31, 64 |
| abstract_inverted_index.augmenting | 181 |
| abstract_inverted_index.boundaries | 187 |
| abstract_inverted_index.disastrous | 14 |
| abstract_inverted_index.minimizing | 11 |
| abstract_inverted_index.predicting | 3 |
| abstract_inverted_index.prediction | 26 |
| abstract_inverted_index.structured | 91 |
| abstract_inverted_index.trajectory | 25 |
| abstract_inverted_index.(CNN+LSTM), | 48 |
| abstract_inverted_index.Time-Series | 50 |
| abstract_inverted_index.Transformer | 51, 54 |
| abstract_inverted_index.alternative | 131 |
| abstract_inverted_index.combination | 38 |
| abstract_inverted_index.prediction. | 194 |
| abstract_inverted_index.significant | 153 |
| abstract_inverted_index.techniques. | 36 |
| abstract_inverted_index.CNN+PatchTST | 157 |
| abstract_inverted_index.Medium-Range | 86 |
| abstract_inverted_index.experimental | 115 |
| abstract_inverted_index.Convolutional | 41 |
| abstract_inverted_index.appropriately | 180 |
| abstract_inverted_index.deep-learning | 35 |
| abstract_inverted_index.(CNN+PatchTST) | 52 |
| abstract_inverted_index.Administration | 77 |
| abstract_inverted_index.Meteorological | 76, 97 |
| abstract_inverted_index.meteorological | 92 |
| abstract_inverted_index.CNN+Transformer | 160 |
| abstract_inverted_index.(CNN+Transformer) | 55 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.85966705 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |