Application of LSTM neural networks with multivariate numerical analysis to aviation wind gust forecasting
DOI: https://doi.org/10.3846/aviation.2026.26809Abstract
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.
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airport meteorology, air traffic management, runway operations, gust forecasting, long short-term memory, feature selection, decision supportHow to Cite
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Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
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