A probabilistic track model for tropical cyclone risk assessment using multitask learning Article Swipe
Zhou Jian
,
Xuan Liu
,
Tianyang Zhao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fenrg.2023.1277412
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fenrg.2023.1277412
Tropical cyclone (TC) track forecasting is critical for wind risk assessment. This work proposes a novel probabilistic TC track forecasting model based on mixture density network (MDN) and multitask learning (MTL). The existing NN-based probabilistic TC track prediction models focus on directly modeling the distribution of the future TC positions. Multitask learning has been shown to boost the performance of single tasks when the tasks are relevant. This work divides the probabilistic track prediction task into two sub-tasks: a deterministic prediction of the future TC position and a probabilistic prediction of the residual between the deterministic prediction and the actual TC location. The MDN is employed to realize the probabilistic prediction task. Since the target values of the MDN in this work are the residuals, which depend on the prediction result of the deterministic task, a novel training method is developed to train the MTL model properly. The proposed model is tested against statistical and other learning-based models on historical TC data. The results show that the proposed model outperforms other models in making probabilistic predictions. This approach advances TC track forecasting by integrating MDN and MTL, showing promise in enhancing probabilistic predictions and improving disaster preparedness.
Related Topics
Concepts
Probabilistic logic
Computer science
Probabilistic forecasting
Machine learning
Statistical model
Track (disk drive)
Task (project management)
Artificial intelligence
Multi-task learning
Tropical cyclone forecast model
Residual
Tropical cyclone
Meteorology
Engineering
Algorithm
Geography
Operating system
Systems engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fenrg.2023.1277412
- OA Status
- gold
- Cited By
- 1
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387184621
All OpenAlex metadata
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https://openalex.org/W4387184621Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fenrg.2023.1277412Digital Object Identifier
- Title
-
A probabilistic track model for tropical cyclone risk assessment using multitask learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-28Full publication date if available
- Authors
-
Zhou Jian, Xuan Liu, Tianyang ZhaoList of authors in order
- Landing page
-
https://doi.org/10.3389/fenrg.2023.1277412Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3389/fenrg.2023.1277412Direct OA link when available
- Concepts
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Probabilistic logic, Computer science, Probabilistic forecasting, Machine learning, Statistical model, Track (disk drive), Task (project management), Artificial intelligence, Multi-task learning, Tropical cyclone forecast model, Residual, Tropical cyclone, Meteorology, Engineering, Algorithm, Geography, Operating system, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W6685448426, https://openalex.org/W2964085201, https://openalex.org/W2494978465, https://openalex.org/W3174661803, https://openalex.org/W2913340405, https://openalex.org/W2963677766, https://openalex.org/W3006369376, https://openalex.org/W2161501662, https://openalex.org/W4303614182, https://openalex.org/W4212807931, https://openalex.org/W3005696531, https://openalex.org/W4212973602, https://openalex.org/W4313576105, https://openalex.org/W2021418005, https://openalex.org/W6801574860, https://openalex.org/W2171979621, https://openalex.org/W2128231293, https://openalex.org/W6656195642, https://openalex.org/W2001673819, https://openalex.org/W3033421405, https://openalex.org/W3114394032, https://openalex.org/W2075385607, https://openalex.org/W4226350101, https://openalex.org/W1986278258, https://openalex.org/W1994616650, https://openalex.org/W1498436455, https://openalex.org/W616584602, https://openalex.org/W2768274284, https://openalex.org/W3047845632, https://openalex.org/W208684393, https://openalex.org/W2003443855, https://openalex.org/W2127016675, https://openalex.org/W6740519964, https://openalex.org/W3088660690, https://openalex.org/W2029223910, https://openalex.org/W2541711215, https://openalex.org/W1579853615 |
| referenced_works_count | 37 |
| abstract_inverted_index.a | 14, 78, 87, 135 |
| abstract_inverted_index.TC | 17, 35, 48, 84, 100, 160, 179 |
| abstract_inverted_index.by | 182 |
| abstract_inverted_index.in | 119, 172, 189 |
| abstract_inverted_index.is | 5, 104, 139, 150 |
| abstract_inverted_index.of | 45, 59, 81, 90, 116, 131 |
| abstract_inverted_index.on | 22, 40, 127, 158 |
| abstract_inverted_index.to | 55, 106, 141 |
| abstract_inverted_index.MDN | 103, 118, 184 |
| abstract_inverted_index.MTL | 144 |
| abstract_inverted_index.The | 31, 102, 147, 162 |
| abstract_inverted_index.and | 27, 86, 97, 154, 185, 193 |
| abstract_inverted_index.are | 65, 122 |
| abstract_inverted_index.for | 7 |
| abstract_inverted_index.has | 52 |
| abstract_inverted_index.the | 43, 46, 57, 63, 70, 82, 91, 94, 98, 108, 113, 117, 123, 128, 132, 143, 166 |
| abstract_inverted_index.two | 76 |
| abstract_inverted_index.(TC) | 2 |
| abstract_inverted_index.MTL, | 186 |
| abstract_inverted_index.This | 11, 67, 176 |
| abstract_inverted_index.been | 53 |
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| abstract_inverted_index.risk | 9 |
| abstract_inverted_index.show | 164 |
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| abstract_inverted_index.this | 120 |
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| abstract_inverted_index.work | 12, 68, 121 |
| abstract_inverted_index.(MDN) | 26 |
| abstract_inverted_index.Since | 112 |
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| abstract_inverted_index.data. | 161 |
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| abstract_inverted_index.model | 20, 145, 149, 168 |
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| abstract_inverted_index.other | 155, 170 |
| abstract_inverted_index.shown | 54 |
| abstract_inverted_index.task, | 134 |
| abstract_inverted_index.task. | 111 |
| abstract_inverted_index.tasks | 61, 64 |
| abstract_inverted_index.track | 3, 18, 36, 72, 180 |
| abstract_inverted_index.train | 142 |
| abstract_inverted_index.which | 125 |
| abstract_inverted_index.(MTL). | 30 |
| abstract_inverted_index.actual | 99 |
| abstract_inverted_index.depend | 126 |
| abstract_inverted_index.future | 47, 83 |
| abstract_inverted_index.making | 173 |
| abstract_inverted_index.method | 138 |
| abstract_inverted_index.models | 38, 157, 171 |
| abstract_inverted_index.result | 130 |
| abstract_inverted_index.single | 60 |
| abstract_inverted_index.target | 114 |
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| abstract_inverted_index.realize | 107 |
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| abstract_inverted_index.showing | 187 |
| abstract_inverted_index.NN-based | 33 |
| abstract_inverted_index.Tropical | 0 |
| abstract_inverted_index.advances | 178 |
| abstract_inverted_index.approach | 177 |
| abstract_inverted_index.critical | 6 |
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| abstract_inverted_index.disaster | 195 |
| abstract_inverted_index.employed | 105 |
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| abstract_inverted_index.modeling | 42 |
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| abstract_inverted_index.proposed | 148, 167 |
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| abstract_inverted_index.enhancing | 190 |
| abstract_inverted_index.improving | 194 |
| abstract_inverted_index.location. | 101 |
| abstract_inverted_index.multitask | 28 |
| abstract_inverted_index.properly. | 146 |
| abstract_inverted_index.relevant. | 66 |
| abstract_inverted_index.historical | 159 |
| abstract_inverted_index.positions. | 49 |
| abstract_inverted_index.prediction | 37, 73, 80, 89, 96, 110, 129 |
| abstract_inverted_index.residuals, | 124 |
| abstract_inverted_index.sub-tasks: | 77 |
| abstract_inverted_index.assessment. | 10 |
| abstract_inverted_index.forecasting | 4, 19, 181 |
| abstract_inverted_index.integrating | 183 |
| abstract_inverted_index.outperforms | 169 |
| abstract_inverted_index.performance | 58 |
| abstract_inverted_index.predictions | 192 |
| abstract_inverted_index.statistical | 153 |
| abstract_inverted_index.distribution | 44 |
| abstract_inverted_index.predictions. | 175 |
| abstract_inverted_index.deterministic | 79, 95, 133 |
| abstract_inverted_index.preparedness. | 196 |
| abstract_inverted_index.probabilistic | 16, 34, 71, 88, 109, 174, 191 |
| abstract_inverted_index.learning-based | 156 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5100362341 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I159948400 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.8399999737739563 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.56939171 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |