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@article{184514,
        author = {Pavan J P and Dr. Kumar Siddamallappa. U and Shreedevi Prakash Gotur},
        title = {THE AEROPILOT SYSTEM FORECASTING TECHNIQUES FOR HARSH LANDING AREAS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {1727-1733},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184514},
        abstract = {More than half of all aircraft operation accidents could have been prevented by executing a go-around. Making timely decision to execute a go-around manoeuvre can potentially reduce overall aviation industry accident rate. In this paper, we describe a cockpit-deployable machine learning system to support flight crew go-around decision-making based on the Forecasting of a Harsh landing event.
This work presents a hybrid approach for Harsh landing Forecasting that uses features modelling temporal dependencies of aircraft variables as inputs to a neural network. It follows that our approach is a cockpit-deployable recommendation system that outperforms existing approach.},
        keywords = {Machine Learning, Forecasting, Harsh landing, Hybrid approach, Cockpit-deployable.},
        month = {September},
        }