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.
Related papers
- Seasonal Fire Prediction using Spatio-Temporal Deep Neural Networks [2.748450182087935]
We utilize SeasFire, a comprehensive global wildfire dataset with climate, vegetation, oceanic indices, and human-related variables, to enable seasonal wildfire forecasting with machine learning.
For the predictive analysis, we train deep learning models with different architectures that capture wildfire-temporal context.
Our findings demonstrate the great potential of deep learning models in seasonal fire forecasting.
arXiv Detail & Related papers (2024-04-09T16:28:54Z) - Comparing Data-Driven and Mechanistic Models for Predicting Phenology in
Deciduous Broadleaf Forests [47.285748922842444]
We train a deep neural network to predict a phenological index from meteorological time series.
We find that this approach outperforms traditional process-based models.
arXiv Detail & Related papers (2024-01-08T15:29:23Z) - SeasFire as a Multivariate Earth System Datacube for Wildfire Dynamics [1.019446914776079]
We release the SeasFire datacube, a meticulously curated dataset tailored for global sub-seasonal wildfire modeling via Earth observation.
The SeasFire datacube comprises of 59 variables encompassing climate, vegetation indices, and human factors, has an 8-day temporal resolution and a spatial resolution of 0.25$circ$, and spans from 2001 to 2021.
We showcase the versatility of SeasFire for exploring the variability and seasonality of wildfire drivers, modeling causal links between ocean-climateconnections and wildfires, and predicting sub-seasonal wildfire patterns across multiple timescales with a Deep Learning model.
arXiv Detail & Related papers (2023-12-12T12:07:34Z) - Residual Diffusion Modeling for Km-scale Atmospheric Downscaling [51.061954281398116]
A cost-effective downscaling model is trained from a high-resolution 2-km weather model over Taiwan.
textitCorrDiff exhibits skillful RMSE and CRPS and faithfully recovers spectra and distributions even for extremes.
Downscaling global forecasts successfully retains many of these benefits, foreshadowing the potential of end-to-end, global-to-km-scales machine learning weather predictions.
arXiv Detail & Related papers (2023-09-24T19:57:22Z) - GraphCast: Learning skillful medium-range global weather forecasting [107.40054095223779]
We introduce a machine learning-based method called "GraphCast", which can be trained directly from reanalysis data.
It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute.
We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets.
arXiv Detail & Related papers (2022-12-24T18:15:39Z) - Insights into the drivers and spatio-temporal trends of extreme
Mediterranean wildfires with statistical deep-learning [0.0]
Recent trends in wildfire activity suggest that wildfires are likely to be highly impacted by climate change.
We analyse monthly burnt area due to wildfires over a region encompassing most of Europe and the Mediterranean Basin from 2001 to 2020.
We use a hybrid statistical deep-learning framework that can disentangle the effects of vapour-pressure deficit, air temperature, and drought on wildfire activity.
arXiv Detail & Related papers (2022-12-04T11:03:25Z) - Neuroevolution-based Classifiers for Deforestation Detection in Tropical
Forests [62.997667081978825]
Millions of hectares of tropical forests are lost every year due to deforestation or degradation.
Monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals.
This paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks.
arXiv Detail & Related papers (2022-08-23T16:04:12Z) - Forecasting large-scale circulation regimes using deformable
convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs)
We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future.
Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
arXiv Detail & Related papers (2022-02-10T11:37:00Z) - Mitigating Greenhouse Gas Emissions Through Generative Adversarial
Networks Based Wildfire Prediction [11.484140660635239]
We develop a deep learning based data augmentation approach for wildfire risk prediction.
By adopting the proposed method, we can take preventive strategies of wildfire mitigation to reduce global GHG emissions.
arXiv Detail & Related papers (2021-08-20T00:36:30Z) - From Static to Dynamic Prediction: Wildfire Risk Assessment Based on
Multiple Environmental Factors [69.9674326582747]
Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States.
We propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California.
arXiv Detail & Related papers (2021-03-14T17:56:17Z) - Modeling Wildfire Perimeter Evolution using Deep Neural Networks [0.0]
We propose a wildfire spreadingmodel that predicts the evolution of the wildfire perimeter in 24 hour periods.
The model is able to learn wildfirespreading dynamics from real historic data sets from wildfires in the Western Sierra Nevada Mountains in Cal-ifornia.
arXiv Detail & Related papers (2020-09-08T20:06:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.