On the use of kolmogorov–arnold networks for adapting wind numerical weather forecasts with explainability and interpretability: application to madeira international airport
This study examines the application of machine learning to enhance wind nowcasting by using a Kolmogorov-Arnold Network model to improve predictions from the Global Forecast System at Madeira International Airport, a site affected by complex terrain. The research addresses the limitations of traditi...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | Environmental Research Communications |
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| Online Access: | https://doi.org/10.1088/2515-7620/ad810f |
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| _version_ | 1823859068403449856 |
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| author | Décio Alves Fábio Mendonça Sheikh Shanawaz Mostafa Fernando Morgado-Dias |
| author_facet | Décio Alves Fábio Mendonça Sheikh Shanawaz Mostafa Fernando Morgado-Dias |
| author_sort | Décio Alves |
| collection | DOAJ |
| description | This study examines the application of machine learning to enhance wind nowcasting by using a Kolmogorov-Arnold Network model to improve predictions from the Global Forecast System at Madeira International Airport, a site affected by complex terrain. The research addresses the limitations of traditional numerical weather prediction models, which often fail to accurately forecast localized wind patterns. Using the Kolmogorov-Arnold Network model led to a substantial reduction in wind speed and direction forecast errors, with a performance that reached a 48.5% improvement to the Global Forecast System 3 h nowcast, considering the mean squared error. A key outcome of this study comes from the model’s ability to generate mathematical formulas that provide insights into the physical and mathematical dynamics influencing local wind patterns and improve the transparency, explainability, and interpretability of the employed machine learning models for atmosphere modeling. |
| format | Article |
| id | doaj-art-d2077647b4844373bf657b9e6293da6f |
| institution | Kabale University |
| issn | 2515-7620 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Communications |
| spelling | doaj-art-d2077647b4844373bf657b9e6293da6f2025-02-11T09:27:10ZengIOP PublishingEnvironmental Research Communications2515-76202024-01-0161010500810.1088/2515-7620/ad810fOn the use of kolmogorov–arnold networks for adapting wind numerical weather forecasts with explainability and interpretability: application to madeira international airportDécio Alves0https://orcid.org/0009-0001-2972-6505Fábio Mendonça1Sheikh Shanawaz Mostafa2Fernando Morgado-Dias3University of Madeira , Campus Universitário da Penteada 9020-105 Funchal, Portugal; Interactive Technologies Institute (ITI/LARSyS and ARDITI) , Edif. Madeira Tecnopolo, Caminho da Penteada piso -2, 9020-105 Funchal, PortugalUniversity of Madeira , Campus Universitário da Penteada 9020-105 Funchal, Portugal; Interactive Technologies Institute (ITI/LARSyS and ARDITI) , Edif. Madeira Tecnopolo, Caminho da Penteada piso -2, 9020-105 Funchal, PortugalInteractive Technologies Institute (ITI/LARSyS and ARDITI) , Edif. Madeira Tecnopolo, Caminho da Penteada piso -2, 9020-105 Funchal, PortugalUniversity of Madeira , Campus Universitário da Penteada 9020-105 Funchal, Portugal; Interactive Technologies Institute (ITI/LARSyS and ARDITI) , Edif. Madeira Tecnopolo, Caminho da Penteada piso -2, 9020-105 Funchal, PortugalThis study examines the application of machine learning to enhance wind nowcasting by using a Kolmogorov-Arnold Network model to improve predictions from the Global Forecast System at Madeira International Airport, a site affected by complex terrain. The research addresses the limitations of traditional numerical weather prediction models, which often fail to accurately forecast localized wind patterns. Using the Kolmogorov-Arnold Network model led to a substantial reduction in wind speed and direction forecast errors, with a performance that reached a 48.5% improvement to the Global Forecast System 3 h nowcast, considering the mean squared error. A key outcome of this study comes from the model’s ability to generate mathematical formulas that provide insights into the physical and mathematical dynamics influencing local wind patterns and improve the transparency, explainability, and interpretability of the employed machine learning models for atmosphere modeling.https://doi.org/10.1088/2515-7620/ad810fkolmogorov-arnold networkwind nowcastingmadeira airportexplainabilityinterpretabilitymachine learning |
| spellingShingle | Décio Alves Fábio Mendonça Sheikh Shanawaz Mostafa Fernando Morgado-Dias On the use of kolmogorov–arnold networks for adapting wind numerical weather forecasts with explainability and interpretability: application to madeira international airport Environmental Research Communications kolmogorov-arnold network wind nowcasting madeira airport explainability interpretability machine learning |
| title | On the use of kolmogorov–arnold networks for adapting wind numerical weather forecasts with explainability and interpretability: application to madeira international airport |
| title_full | On the use of kolmogorov–arnold networks for adapting wind numerical weather forecasts with explainability and interpretability: application to madeira international airport |
| title_fullStr | On the use of kolmogorov–arnold networks for adapting wind numerical weather forecasts with explainability and interpretability: application to madeira international airport |
| title_full_unstemmed | On the use of kolmogorov–arnold networks for adapting wind numerical weather forecasts with explainability and interpretability: application to madeira international airport |
| title_short | On the use of kolmogorov–arnold networks for adapting wind numerical weather forecasts with explainability and interpretability: application to madeira international airport |
| title_sort | on the use of kolmogorov arnold networks for adapting wind numerical weather forecasts with explainability and interpretability application to madeira international airport |
| topic | kolmogorov-arnold network wind nowcasting madeira airport explainability interpretability machine learning |
| url | https://doi.org/10.1088/2515-7620/ad810f |
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