Spatio-Temporal Wildfire Prediction Using Multi-Modal Data
Spatio-Temporal Wildfire Prediction Using Multi-Modal Data
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DOI：
10.1109/jsait.2023.3276054
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Chen Xu;Yao Xie;Daniel A. Zuniga Vazquez;Rui Yao;Feng Qiu
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文献类型：
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作者：
Chen Xu;Yao Xie;Daniel A. Zuniga Vazquez;Rui Yao;Feng Qiu
由于严重的社会和环境影响，使用多模态传感数据进行野火预测已成为各种利益相关者（如州政府和电力公司）非常抢手的数据分析工具，以更好地了解野火活动并计划预防措施。一个理想的算法应该精确地预测火灾风险和规模的位置在真实的时间。在本文中，我们开发了一个灵活的时空野火预测框架，使用多模态时间序列数据。我们首先预测的野火风险（野火事件的机会）在实时，考虑到历史事件使用离散的相互激励点过程模型。在此基础上，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）方法的野火震级预测集方法。在理论上，我们证明了风险模型的参数恢复保证，以及覆盖率和集大小保证的CP集。通过广泛的真实数据实验与野火数据在加州，我们证明了我们的方法的有效性，以及它们的灵活性和可扩展性在大区域。
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.