Wildfire Danger Prediction and Understanding With Deep Learning
2022
Kondylatos, Spyros | Prapas, Ioannis | Ronco, Michele | Papoutsis, Ioannis | Camps-Valls, Gustau | Piles, María | Fernández-Torres, Miguel Ángel | Carvalhais, Nuno | DCEA - Departamento de Ciências e Engenharia do Ambiente
The authors thank Fabian Gans who provided the instructions to deploy the data cube in a cloud‐optimized format. Publisher Copyright: © 2022 The Authors.
Afficher plus [+] Moins [-]Climate change exacerbates the occurence of extreme droughts and heatwaves, increasing the frequency and intensity of large wildfires across the globe. Forecasting wildfire danger and uncovering the drivers behind fire events become central for understanding relevant climate-land surface feedback and aiding wildfire management. In this work, we leverage Deep Learning (DL) to predict the next day's wildfire danger in a fire-prone part of the Eastern Mediterranean and explainable Artificial Intelligence (xAI) to diagnose model attributions. We implement DL models that capture the temporal and spatio-temporal context, generalize well for extreme wildfires, and demonstrate improved performance over the traditional Fire Weather Index. Leveraging xAI, we identify the substantial contribution of wetness-related variables and unveil the temporal focus of the models. The variability of the contribution of the input variables across wildfire events hints into different wildfire mechanisms. The presented methodology paves the way to more robust, accurate, and trustworthy data-driven anticipation of wildfires.
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Afficher plus [+] Moins [-]published
Afficher plus [+] Moins [-]Mots clés AGROVOC
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