Seasonal Fire Prediction using Spatio-Temporal Deep Neural Networks

TL;DR

This paper explores the use of deep neural networks with spatio-temporal data to predict wildfires up to six months in advance, emphasizing the importance of input length and spatial context for improved accuracy.

Contribution

It introduces a comprehensive global wildfire dataset and evaluates deep learning architectures for seasonal wildfire prediction, highlighting the benefits of longer input sequences and spatial information.

Findings

Longer input time-series improve prediction robustness.

Incorporating spatial context enhances model performance.

Larger receptive fields are needed for longer-term forecasts.

Abstract

With climate change expected to exacerbate fire weather conditions, the accurate anticipation of wildfires on a global scale becomes increasingly crucial for disaster mitigation. In this study, 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 the spatio-temporal context leading to wildfires. Our investigation focuses on assessing the effectiveness of these models in predicting the presence of burned areas at varying forecasting time horizons globally, extending up to six months into the future, and on how different spatial or/and temporal context affects the performance of the models. Our findings demonstrate the great potential of deep learning models in seasonal fire forecasting; longer input time-series leads to more robust predictions across varying forecasting horizons, while integrating spatial information to capture wildfire spatio-temporal dynamics boosts performance. Finally, our results hint that in order to enhance performance at longer forecasting horizons, a larger receptive field spatially needs to be considered.