Deep Learning Methods for Daily Wildfire Danger Forecasting

• URL: http://arxiv.org/abs/2111.02736v1
• Date: Thu, 4 Nov 2021 10:39:12 GMT
• Title: Deep Learning Methods for Daily Wildfire Danger Forecasting
• Authors: Ioannis Prapas, Spyros Kondylatos, Ioannis Papoutsis, Gustau Camps-Valls, Michele Ronco, Miguel-\'Angel Fern\'andez-Torres, Maria Piles Guillem, Nuno Carvalhais
• Abstract summary: 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 fire danger. Our DL-based proof-concept provides national-scale daily fire danger maps at a much spatial higher resolution than existing operational solutions.
• Score: 6.763972119525753
• License: http://creativecommons.org/licenses/by/4.0/
• 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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