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APPLICATION OF LSTM NEURAL NETWORKS WITH MULTIVARIATE NUMERICAL ANALYSIS TO AVIATION WIND GUST FORECASTING.

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Title: APPLICATION OF LSTM NEURAL NETWORKS WITH MULTIVARIATE NUMERICAL ANALYSIS TO AVIATION WIND GUST FORECASTING.
Authors: CHEN, Chuen-Jyh1 chuenjyh@mail.cjcu.edu.tw
Source: Aviation (1648-7788). 2026, Vol. 30 Issue 2, p143-155. 13p.
Subjects: Wind forecasting, Recurrent neural networks, Weather hazards, Weather forecasting, Forecasting, Feature selection, Multivariate analysis
Abstract: This paper presents a long short-term memory (LSTM) framework developed for predicting wind gusts 1 h in advance at Taiwan Taoyuan International Airport (RCTP) during typhoons. Hourly surface observations were collected from 12 landfalling typhoons (2010-2020) and used to compare three feature-selection strategies: Pearson correlation, recursive feature elimination with cross validation, and random-forest importance. Models were trained on 12-h multivariate histories. A leave-one-typhoon-out cross-validation scheme revealed that the LSTM model with random-forest selection achieved a mean root-mean-square error of 2.33 m/s and mean absolute percentage error of 21.12%. Although these statistics are comparable to those of a 1-h persistence baseline model on average, the proposed model considerably outperformed the persistence baseline model during rapid intensification and decay phases, reducing errors by approximately 45%. Forecast errors generally remained within the ±5 m/s operational advisory threshold. The results of this case study for RCTP suggest that feature selection can be combined with sequence-based deep learning to provide robust decision support for aviation operations during extreme weather events. [ABSTRACT FROM AUTHOR]
Copyright of Aviation (1648-7788) is the property of Vilnius Gediminas Technical University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: APPLICATION OF LSTM NEURAL NETWORKS WITH MULTIVARIATE NUMERICAL ANALYSIS TO AVIATION WIND GUST FORECASTING.
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  Data: <searchLink fieldCode="AR" term="%22CHEN%2C+Chuen-Jyh%22">CHEN, Chuen-Jyh</searchLink><relatesTo>1</relatesTo><i> chuenjyh@mail.cjcu.edu.tw</i>
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  Data: <searchLink fieldCode="JN" term="%22Aviation+%281648-7788%29%22">Aviation (1648-7788)</searchLink>. 2026, Vol. 30 Issue 2, p143-155. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Wind+forecasting%22">Wind forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Weather+hazards%22">Weather hazards</searchLink><br /><searchLink fieldCode="DE" term="%22Weather+forecasting%22">Weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper presents a long short-term memory (LSTM) framework developed for predicting wind gusts 1 h in advance at Taiwan Taoyuan International Airport (RCTP) during typhoons. Hourly surface observations were collected from 12 landfalling typhoons (2010-2020) and used to compare three feature-selection strategies: Pearson correlation, recursive feature elimination with cross validation, and random-forest importance. Models were trained on 12-h multivariate histories. A leave-one-typhoon-out cross-validation scheme revealed that the LSTM model with random-forest selection achieved a mean root-mean-square error of 2.33 m/s and mean absolute percentage error of 21.12%. Although these statistics are comparable to those of a 1-h persistence baseline model on average, the proposed model considerably outperformed the persistence baseline model during rapid intensification and decay phases, reducing errors by approximately 45%. Forecast errors generally remained within the ±5 m/s operational advisory threshold. The results of this case study for RCTP suggest that feature selection can be combined with sequence-based deep learning to provide robust decision support for aviation operations during extreme weather events. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Aviation (1648-7788) is the property of Vilnius Gediminas Technical University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.3846/aviation.2026.26809
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 13
        StartPage: 143
    Subjects:
      – SubjectFull: Wind forecasting
        Type: general
      – SubjectFull: Recurrent neural networks
        Type: general
      – SubjectFull: Weather hazards
        Type: general
      – SubjectFull: Weather forecasting
        Type: general
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Feature selection
        Type: general
      – SubjectFull: Multivariate analysis
        Type: general
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      – TitleFull: APPLICATION OF LSTM NEURAL NETWORKS WITH MULTIVARIATE NUMERICAL ANALYSIS TO AVIATION WIND GUST FORECASTING.
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            – D: 01
              M: 04
              Text: 2026
              Type: published
              Y: 2026
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              Value: 30
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