Deep Learning for Global Wildfire Forecasting

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 subseasonal 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.

Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction

“…More than 5000 images captured by the Himawari-8 satellite between November 2019 and February 2020 in the Australian regions were employed as training data, achieving an accuracy of greater than 80%. Prapas et al [155] also applied U-Net++ as a global wildfire forecasting method. Using the seasFire cube dataset [156], which includes variables related to fire such as historical burned areas and fire emissions between 2001 and 2021, climate, vegetation, oceanic indices, and human related data, U-Net++ reached an F1-score of 50.7%.…”

Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables

“…The existing research has delved into various domains of wildfire prediction, such as fire occurrence prediction [24,25], fire severity [26,27], prediction of the burn area or susceptibility [28][29][30], and fire spread prediction [31][32][33]. Researchers have been committed to providing predictions of combustion at different lead times, including short-term [19,34,35] and long-term [36][37][38][39].…”

“…A combination of CNNs and Long Short-Term Memory (LSTM) models has been used to model global burned areas and global wildfire susceptibility [63,64]. The use of U-Net networks has been seen in predicting global wildfire danger [29] and combining global information and teleconnection for global wildfire prediction [65]. Additionally, a multibranch network has been used to predict wildfire danger in the Mediterranean Sea [22].…”