Fire Hazard Forecasting

Orion Lab has developed deep learning methods for daily wildfire danger forecasting. We approach daily fire danger prediction as a machine learning task, using historical Earth observation data from the last decade to predict next-day’s fire danger. Our work aims at producing fire hazard forecasts at large scales and with better spatial resolution compared to other existing methods and operational tools.

We implement a variety of Deep Learning (DL) models to capture the spatial, temporal or spatio-temporal context and compare them against a Random Forest (RF) baseline and the fire weather index

Our DL-based proof-of-concept provides national-scale daily fire danger maps at a much higher spatial resolution than existing operational solutions.

Figure: Example of a DL-powered fire danger map.

We use explainable Artificial Intelligence methods on top of our DL models to gain more insights about the predictions. This allows us to answer important questions, for example:

• which are the main drivers?
• are there meaningful space/time patterns that increase fire risk?
• are there ways to know how sure the model is about a prediction?
• what is the impact of a change of a predictor on the fire risk?