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@article{188767,
        author = {Disha S and Bhagyashri K Kulkarni and Anjana R C and D Mahesh and Koushik N},
        title = {Unified Aviation Weather Intelligence System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {4866-4868},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188767},
        abstract = {The aviation industry faces critical challenges due to unpredictable weather and turbulence, often resulting in flight delays, safety concerns, and increased operational costs. Traditional systems treat weather forecasting and route planning as separate entities, limiting their effectiveness. This project proposes a unified platform that integrates hyper-local weather monitoring, machine learning-based turbulence prediction, and real-time route optimization. By leveraging real-time data from flight tracking APIs and live weather feeds, the system provides actionable insights through intelligent automation and visualization dashboards tailored for airline operators and pilots. Machine learning models such as LSTM and Dijkstra’s algorithm are employed to predict turbulence and suggest optimal routing, enhancing flight safety,fuel efficiency, and overall passenger comfort.
The solution is built using modern technologies including ReactJS, Python, FastAPI and Leaflet. This comprehensive approach aims to revolutionize aviation planning and risk management through intelligent, data-driven decision-making.},
        keywords = {Aviation, Turbulence, Weather, Safety},
        month = {March},
        }