Due to severe societal and environmental impacts, wildfire prediction using multi-modal sensing data has become a highly sought-after data-analytical tool by various stakeholders (such as state governments and power utility companies) to achieve a more informed understanding of wildfire activities and plan preventive measures. A desirable algorithm should precisely predict fire risk and magnitude for a location in real time. In this paper, we develop a flexible spatio-temporal wildfire prediction framework using multi-modal time series data. We first predict the wildfire risk (the chance of a wildfire event) in real-time, considering the historical events using discrete mutually exciting point process models. Then we further develop a wildfire magnitude prediction set method based on the flexible distribution-free time-series conformal prediction (CP) approach. Theoretically, we prove a risk model parameter recovery guarantee, as well as coverage and set size guarantees for the CP sets. Through extensive real-data experiments with wildfire data in California, we demonstrate the effectiveness of our methods, as well as their flexibility and scalability in large regions.

翻译：由于会造成严重的社会和环境危害，利用多模态传感数据进行野火预测已成为各利益相关方（如州政府和电力公用事业公司）高度依赖的数据分析工具，以更深入地了解野火活动并规划预防措施。理想的算法应能实时精确预测特定地点的火灾风险与火势规模。本文提出了一种灵活的多模态时序数据时空野火预测框架。首先，我们基于离散互激点过程模型，结合历史事件，实时预测野火风险（即野火事件发生的概率）。随后，我们进一步发展了一种基于灵活的无分布时间序列共形预测（CP）方法的野火规模预测集合方法。理论上，我们证明了风险模型的参数恢复保证，以及共形预测集合的覆盖率和集合大小保证。通过在加利福尼亚州野火数据上的大规模真实数据实验，我们展示了所提方法的有效性及其在大范围区域内的灵活性与可扩展性。