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

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Bibliographic Details
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]
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Database:	Engineering Source

Description
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]
ISSN:	16487788
DOI:	10.3846/aviation.2026.26809