FireRiskMap

Map-based Forest Fire Risk Assessment

Dryad aims to develop a cutting-edge, map-based system to accurately model forest fire risks and suggest targeted prevention strategies. Unlike existing services that only assess meteorological potentials and use error-prone models, Dryad’s system will employ machine learning for precise, real-time risk assessments and actionable advice, all at a granular one-hectare resolution.

Dryad's FireRiskMap will be designed to provide:

• Development of Machine Learning Models: Dryad intends to construct sophisticated machine learning models that analyze vast datasets beyond mere weather information. These models will consider factors such as topography, vegetation, soil conditions, and human activity to predict actual forest fire risks with high granularity.

• Granular Risk Assessment: Unlike existing systems that offer broad, area-level risk assessments, Dryad’s system will provide detailed risk evaluations down to one hectare per grid cell. This high-resolution mapping allows for more targeted and effective risk mitigation strategies.

• Real-Time Risk Modeling: The information system will be capable of modeling forest fire risks in real-time, enabling quick responses to emerging threats. This feature is critical for timely interventions and efficient resource allocation during high-risk periods.

• Analysis and Simulation Tools: Dryad plans to integrate analysis and simulation tools within the platform, enabling users to perform detailed risk analyses and model the effectiveness of various prevention measures under different scenarios. This will help in planning and implementing effective silvicultural and technical strategies tailored to specific forest areas.

Project Partner

This Project is supported by the Federal Ministry for Economic Affairs and Climate Action (BMWK) on the basis of a decision by the German Bundestag.