Deep Learning for Global Wildfire Forecasting
- URL: http://arxiv.org/abs/2211.00534v3
- Date: Mon, 16 Oct 2023 13:01:58 GMT
- Title: Deep Learning for Global Wildfire Forecasting
- Authors: Ioannis Prapas, Akanksha Ahuja, Spyros Kondylatos, Ilektra Karasante,
Eleanna Panagiotou, Lazaro Alonso, Charalampos Davalas, Dimitrios Michail,
Nuno Carvalhais, Ioannis Papoutsis
- Abstract summary: We create a global fire dataset and demonstrate a prototype for predicting the presence of global burned areas on a sub-seasonal scale.
We present an open-access global analysis-ready datacube, which contains a variety of variables related to the seasonal and sub-seasonal fire drivers.
We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time.
- Score: 1.6929753878977016
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Climate change is expected to aggravate wildfire activity through the
exacerbation of fire weather. Improving our capabilities to anticipate
wildfires on a global scale is of uttermost importance for mitigating their
negative effects. In this work, we create a global fire dataset and demonstrate
a prototype for predicting the presence of global burned areas on a
sub-seasonal scale with the use of segmentation deep learning models.
Particularly, we present an open-access global analysis-ready datacube, which
contains a variety of variables related to the seasonal and sub-seasonal fire
drivers (climate, vegetation, oceanic indices, human-related variables), as
well as the historical burned areas and wildfire emissions for 2001-2021. We
train a deep learning model, which treats global wildfire forecasting as an
image segmentation task and skillfully predicts the presence of burned areas 8,
16, 32 and 64 days ahead of time. Our work motivates the use of deep learning
for global burned area forecasting and paves the way towards improved
anticipation of global wildfire patterns.
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