Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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E-Pilots: A Hybrid LSTM Model For Proactive Hard Landing Detection

Author(s) Ms. Gunn Bansal, Mr. Tushar Gupta, Ms. Tanya Bansal, Ms. Sneha Dhyani, Dr. Satendra Kumar
Country India
Abstract In commercial aviation, hard landings are a recurring safety risk that can result in structural damage, increased maintenance costs, and injuries to passengers or crew. Real-time action is not possible with current safety standards since they mostly rely on post-flight data processing. In order to forecast the likelihood of a hard landing occurrence during the approach phase, this study presents E-Pilots, a machine learning system that may be deployed in the cockpit. E-Pilots attains an average sensitivity of 85% and specificity of 74% at the go-around Decision Height by utilizing a hybrid neural network architecture trained on 58,177 commercial flights across three Airbus models (A319, A320, and A321). In order to provide pilots with early warnings that allow them to perform corrective maneuvers or initiate a go-around, the system analyzes real-time flight characteristics such as vertical speed, descent angle, and route deviation. In order to show that classification-based techniques significantly outperform regression models for early hard landing detection, this study describes the system architecture, dataset characteristics, feature engineering process, and experimental findings.
Keywords Hard landings, aviation safety, machine learning, flight data analysis, real-time prediction, cockpit-deployable systems, predictive modeling, and hybrid neural networks.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-18
DOI https://doi.org/10.63363/aijfr.2026.v07i03.5845
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E-ISSN 3048-7641
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