Data-Driven Prediction and Visualisation of Dynamic Bushfire Risks

Laura Irina Rusu, Hoang Tam Vo, Ziyuan Wang, Mahsa Salehi, Anna Phan

Abstract: The potential impact of bushfires is a significant concern for communities and fire response agencies, and the ability to predict the fire risk timely and accurately is critical. However, that cannot be achieved without accessing and processing very large amounts of data in almost real time. We demonstrate a data-driven fire risk prediction system that leverages big geospatial and meteorological data, where the results are visualised and made available to communities and fire agencies for risk mitigation strategies.