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Thunderstorm predic...
Thunderstorm prediction during pre-tactical air-traffic-flow management using convolutional neural networks
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- Jardines, Aniel (författare)
- Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid
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- Eivazi, Hamidreza (författare)
- KTH,Teknisk mekanik
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- Zea, Elias, 1989- (författare)
- KTH,Teknisk mekanik
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- García-Heras, Javier (författare)
- Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid
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- Simarro, Juan (författare)
- Agencia Estatal de Meteorología (AEMET)
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- Otero, Evelyn, 1983- (författare)
- KTH,Teknisk mekanik
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- Soler, Manuel (författare)
- Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid
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- Vinuesa, Ricardo (författare)
- KTH,Teknisk mekanik
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(creator_code:org_t)
- Elsevier BV, 2024
- 2024
- Engelska.
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Ingår i: Expert systems with applications. - : Elsevier BV. - 0957-4174 .- 1873-6793. ; 241, s. 122466-
- Relaterad länk:
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https://doi.org/10.1...
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https://kth.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Thunderstorms can be a large source of disruption for European air-traffic management causing a chaotic state of operation within the airspace system. In current practice, air-traffic managers are provided with imprecise forecasts which limit their ability to plan strategically. As a result, weather mitigation is performed using tactical measures with a time horizon of three hours. Increasing the lead time of thunderstorm predictions to the day before operations could help air-traffic managers plan around weather and improve the efficiency of air-traffic-management operations. Emerging techniques based on machine learning have provided promising results, partly attributed to reduced human bias and improved capacity in predicting thunderstorms purely from numerical weather prediction data. In this paper, we expand on our previous work on thunderstorm forecasting, by applying convolutional neural networks (CNNs) to exploit the spatial characteristics embedded in the weather data. The learning task of predicting convection is formulated as a binary-classification problem based on satellite data. The performance of multiple CNN-based architectures, including a fully-convolutional neural network (FCN), a CNN-based encoder–decoder, a U-Net, and a pyramid-scene parsing network (PSPNet) are compared against a multi-layer-perceptron (MLP) network. Our work indicates that CNN-based architectures improve the performance of point-prediction models, with a fully-convolutional neural-network architecture having the best performance. Results show that CNN-based architectures can be used to increase the prediction lead time of thunderstorms. Lastly, a case study illustrating the applications of convection-prediction models in an air-traffic-management setting is presented.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Rymd- och flygteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Aerospace Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Thunderstorms
- Air-traffic management
- Weather data
- Numerical weather prediction
- Satellite images
- Convolutional neural network
- Machine learning
- Teknisk mekanik
- Engineering Mechanics
- Aerospace Engineering
- Flyg- och rymdteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Jardines, Aniel
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Eivazi, Hamidrez ...
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Zea, Elias, 1989 ...
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García-Heras, Ja ...
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Simarro, Juan
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Otero, Evelyn, 1 ...
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visa fler...
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Soler, Manuel
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Vinuesa, Ricardo
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visa färre...
- Om ämnet
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- TEKNIK OCH TEKNOLOGIER
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TEKNIK OCH TEKNO ...
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och Maskinteknik
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och Rymd och flygtek ...
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- NATURVETENSKAP
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NATURVETENSKAP
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och Data och informa ...
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och Datavetenskap
- Artiklar i publikationen
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Expert systems w ...
- Av lärosätet
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