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：由于对社会和环境的严重影响，利用多模态传感数据进行野火预测已成为各类利益相关方（如州政府和电力公用事业公司）高度关注的数据分析工具，旨在更深入地理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某一地点的火灾风险与强度。本文利用多模态时间序列数据，开发了一个灵活的时空野火预测框架。我们首先基于历史事件，采用离散相互激励点过程模型实时预测野火风险（即野火事件发生的概率）。随后，进一步开发了一种基于灵活无分布时间序列共形预测（CP）方法的野火强度预测集方法。理论上，我们证明了风险模型参数恢复的保证性，以及共形预测集覆盖率和集合大小的理论保证。通过利用加利福尼亚州野火数据进行的大规模真实数据实验，我们验证了所提方法的有效性，及其在大区域中的灵活性与可扩展性。