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Found 5 papers, 1 papers with code
Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement
To the best of our knowledge, this study represents a pioneering effort in using Reinforcement Learning to address the aforementioned problem, offering promising perspectives in fire prevention and landscape management
Comparison of metaheuristics for the firebreak placement problem: a simulation-based optimization approach
The problem of firebreak placement is crucial for fire prevention, and its effectiveness at landscape scale will depend on their ability to impede the progress of future wildfires.
Two Scalable Approaches for Burned-Area Mapping Using U-Net and Landsat Imagery
This study explores two proposed approaches based on the U-Net model for automating and optimizing the burned-area mapping process.
Adjusting Rate of Spread Factors through Derivative-Free Optimization: A New Methodology to Improve the Performance of Forest Fire Simulators
In practical applications, it is common that wildfire simulators do not correctly predict the evolution of the fire scar.
Cell2Fire: A Cell Based Forest Fire Growth Model
Cell2Fire is a new cell-based forest and wildland landscape fire growth simulator that is open-source and exploits parallelism to support the modelling of fire growth cross large spatial and temporal scales in a timely manner.
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