Seasonal Fire Prediction using Spatio-Temporal Deep Neural Networks
Dimitrios Michail Lefki-Ioanna Panagiotou Charalampos Davalas Ioannis Prapas Spyros Kondylatos Nikolaos Ioannis Bountos Ioannis Papoutsis
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.