Deep Learning Methods for Daily Wildfire Danger Forecasting

Ioannis Prapas; Spyros Kondylatos; Ioannis Papoutsis; Gustau Camps-Valls; Michele Ronco; Miguel-Ángel Fernández-Torres; Maria Piles Guillem; Nuno Carvalhais

arXiv · 2111.02736

Deep Learning Methods for Daily Wildfire Danger Forecasting

Ioannis Prapas·Spyros Kondylatos·Ioannis Papoutsis·Gustau Camps-Valls·Michele Ronco·Miguel-Ángel Fernández-Torres·Maria Piles Guillem·Nuno Carvalhais

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Abstract

Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability. 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. To that end, we collect, pre-process and harmonize an open-access datacube, featuring a set of covariates that jointly affect the fire occurrence and spread, such as weather conditions, satellite-derived products, topography features and variables related to human activity. 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. We find that either spatial or temporal context is enough to surpass the RF, while a ConvLSTM that exploits the spatio-temporal context performs best with a test Area Under the Receiver Operating Characteristic of 0.926. Our DL-based proof-of-concept provides national-scale daily fire danger maps at a much higher spatial resolution than existing operational solutions.

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Ioannis Prapas, Spyros Kondylatos, Ioannis Papoutsis, Gustau Camps-Valls, Michele Ronco, Miguel-Ángel Fernández-Torres, Maria Piles Guillem, Nuno Carvalhais. 2021-11-04. Deep Learning Methods for Daily Wildfire Danger Forecasting. https://arxiv.org/abs/2111.02736

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