Motivation

Wildfires are a severe threat across Europe, causing significant environmental and economic damage. They are becoming more intense and widespread as a result of climate change, particular forestry practices, ecosystem deterioration, and rural depopulation. BAM is member of a large-scale EU Green Deal project with 47 partners from 13 European countries and Taiwan, that covers all three stages of fire management – namely prevention, detection and restoration. A holistic Fire Management Ecosystem is key to implement effective and sustainable actions in the fight against wildfires.

Objectives

In the EU project [1] BAM eScience contributes to the work packages ‘prevention and preparedness’ and ‘detection and response’ on detecting emerging critical situations and forecasting fire and smoke propagation with Machine Learning (ML) techniques. A context-aware detection of emerging fire-related situations will be based on real-time and heterogenous sensor data that capture e.g., weather and soil conditions. They may additionally include factors like structural causes rooted in land and urban planning, human activities in the vicinity of forests and cultural traditions that are typically not considered by current systems. A real-time fire and smoke propagation forecasting system will be developed to support timely decisions and improve firefighting tactics. Physical fire and smoke spread models, which provide accurate predictions, are computationally too expensive for real-time forecasting and mitigation strategies. We will translate physical models into ML models for accurate real-time predictions. These models incorporate knowledge about vegetation and environmental conditions and will incorporate experimental data from experiments performed at BAM.

References

[1] https://dryads-project.eu/
https://twitter.com/DryadsH2020
https://www.linkedin.com/company/dryads-h2020/