论文标题
使用多模式数据的时空野火预测
Spatio-Temporal Wildfire Prediction using Multi-Modal Data
论文作者
论文摘要
由于严重的社会和环境影响，使用多模式感应数据的野火预测已成为各种利益相关者（例如州政府和电力公司公司）的高度渴望的数据分析工具，以实现对野火活动的更加知情的理解和计划预防措施。理想的算法应精确地预测位置的火灾风险和大小。在本文中，我们使用多模式时间序列数据开发了灵活的时空野火预测框架。我们首先考虑使用离散的相互激动人心的点过程模型的历史事件，预测实时的野火风险（野火事件的机会）。然后，我们基于无柔性分布时间序列的保形预测（CP）方法，进一步开发了一种野火幅度预测集方法。从理论上讲，我们证明了风险模型参数恢复保证，以及CP集的覆盖范围和设置尺寸保证。通过加利福尼亚州的野火数据进行广泛的Real-DATA实验，我们证明了方法的有效性以及它们在大型地区的灵活性和可伸缩性。
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.
