Wind Limitations at Madeira International Airport: A Deep Learning Prediction Approach
Abstract
The unique geographical and topographical features of Madeira International Airport in Portugal significantly influence flight safety, primarily due to variable wind patterns. In this study, a machine learning approach is developed to predict runway operational statuses at Madeira International Airport, focusing on addressing wind-related challenges. To tackle this issue, a Deep Learning model is utilized. This model undergoes a particle swarm optimization process, resulting in one optimized model for each timestep, to provide minute-resolution predictions within a 20-minute timeframe. The training, validation, and testing phases for the optimized models were conducted using high-frequency wind data from Madeira International Airport. The main objective is to accurately predict the runway operational statuses, specifically whether the airport is open or closed for landing, take-off, or both. The models exhibit high performance, particularly in identifying operational conditions, reaching 99.93% precision, and a top accuracy of 94.35% predicting all runway status, underscoring their potential to enhance decision-making processes and operational efficiency under challenging weather conditions.
More information
- Authors
- Décio Alves; Diogo Freitas; Fábio Mendonça; Sheikh Mostafa; Fernando Morgado-Dias
- Date
- 2024
- Journal
- IEEE Access
- Publisher
- IEEE
- Source
- Link
Citation
@article{decio2024,
author = {Alves, Décio and Freitas, Diogo and Mendonça, Fábio and Mostafa, Sheikh and Morgado-Dias, Fernando},
doi = {10.1109/ACCESS.2024.3394447},
journal = {IEEE Access},
month = {5},
pages = {61211--61220},
title = {Wind Limitations at Madeira International Airport: A Deep Learning Prediction Approach},
volume = {12},
year = {2024}
}
