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Emulation of wildland fire spread simulation using deep learning.

Frédéric Allaire¹, Vivien Mallet¹, Jean-Baptiste Filippi²

¹Institut national de recherche en informatique et en automatique (INRIA), 2 rue Simone Iff, Paris, France; Sorbonne Université, Laboratoire Jacques-Louis Lions, France.

Neural Networks : the Official Journal of the International Neural Network Society

|April 27, 2021

Summary

This study introduces a deep neural network to rapidly predict wildland fire spread, significantly reducing computational time for fire danger mapping. The developed emulator achieves thousands of times speed-up, enabling near real-time predictions for large areas.

Keywords:

Corsica Deep neural network Fire growth prediction Hybrid architecture Mixed inputs Numerical simulation

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Area of Science:

Environmental Science
Computational Science
Artificial Intelligence

Background:

Wildland fire spread simulation is crucial for operational decision-making and planning.
Traditional simulators face computational time limitations for large-scale, short-term applications like fire danger mapping.

Purpose of the Study:

To develop a computationally efficient emulator for wildland fire spread prediction.
To enable rapid generation of burned area maps for short-term fire danger assessment.

Main Methods:

Utilized a deep neural network with a hybrid architecture to emulate fire spread.
Input data included spatial landscape fields and scalar environmental conditions.
Trained the network on a large dataset of fire simulations.

Main Results:

The emulator achieved a Mean Absolute Percentage Error (MAPE) of 32.8% on a test dataset.
Demonstrated a speed-up factor of several thousands compared to traditional simulators.
Enabled one-hour burned area map computation for an entire island in under a minute.

Conclusions:

The developed deep learning emulator significantly reduces computational time for wildland fire spread prediction.
This approach facilitates new applications in short-term fire danger mapping and crisis management.