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.

利用机器学习进行深度学习模型的季节性火灾预报，研究发现在全球范围内，考虑更长时间序列和整合空间信息可以提高火灾预测性能，并建议在更长的预测时限内考虑更大的空间范围以提高预报准确性。

基于时空深度神经网络的季节性火灾预测

Seasonal Fire Prediction using Spatio-Temporal Deep Neural Networks

Seasonal forecasting is a crucial task when it comes to detecting the extreme
heat and colds that occur due to climate change. Confidence in the predictions
should be reliable since a small increase in the temperatures in a year has a
big impact on the world. Calibration of the neural networks provides a way to
ensure our confidence in the predictions. However, calibrating regression
models is an under-researched topic, especially in forecasters. We calibrate a
UNet++ based architecture, which was shown to outperform physics-based models
in temperature anomalies. We show that with a slight trade-off between
prediction error and calibration error, it is possible to get more reliable and
sharper forecasts. We believe that calibration should be an important part of
safety-critical machine learning applications such as weather forecasters.

季节性预测是探测气候变化引起的极端炎热与寒冷的关键任务。我们应该对预测结果有可靠的信心，因为每年温度增加一点点都对世界产生巨大影响。神经网络的校准提供了确保预测可信度的方法，然而，在预测中校准回归模型是一个鲜为人知的课题。我们通过校准基于 UNet++ 架构的模型，证明相对于基于物理的模型在温度异常方面表现更优。我们发现，在预测误差和校准误差之间做一定权衡，可以得到更可靠、更精确的预测。我们相信，在天气预报等安全关键的机器学习应用中，校准应该是一个重要部分。