基于可解释机器学习模型的全球闪电引发野火预测与气候变化预估
Global Lightning-Ignited Wildfires Prediction and Climate Change Projections based on Explainable Machine Learning Models
AI总结：
本研究构建全球尺度闪电引发野火的机器学习预测模型，结合XAI分析特征影响，发现气候变化已在短时间内提升该类野火全球风险，凸显分类构建预测模型的必要性。
摘要：
野火对人类构成重大自然灾害风险，还会加剧气候变化进程。由于野火同样受气候变化影响，极端野火事件正变得愈发频繁。尽管全球范围内闪电引发的野火发生频次低于人为活动引发的野火，但它在碳排放中占比颇高，且在部分区域贡献了绝大多数的过火面积。现有计算模型尤其是基于机器学习的模型虽旨在预测闪电引发的野火，但通常仅针对具有独特特征的特定区域构建，限制了其全球适用性。本研究提出了旨在在全球尺度上表征与预测闪电引发野火的机器学习模型。我们的方法包括对闪电引发野火与人为引发野火进行分类，并基于气象条件、植被等多类广泛因素，高精度估算闪电引燃野火的概率。借助这些模型，我们分析了闪电引发野火的季节与空间趋势，揭示了气候变化对这一现象的影响。我们利用可解释人工智能（eXplainable Artificial Intelligence, XAI）框架分析了各类特征对模型的影响。研究结果凸显了人为野火与闪电引发野火在全球范围内存在显著差异。此外，我们证明，即便在不到十年的短时间跨度内，气候变化已稳步提升了全球范围内闪电引发野火的风险。这一差异凸显了针对每类野火构建专用预测模型与火险天气指数的迫切必要性。
英文摘要：
Wildfires pose a significant natural disaster risk to populations and contribute to accelerated climate change. As wildfires are also affected by climate change, extreme wildfires are becoming increasingly frequent. Although they occur less frequently globally than those sparked by human activities, lightning-ignited wildfires play a substantial role in carbon emissions and account for the majority of burned areas in certain regions. While existing computational models, especially those based on machine learning, aim to predict lightning-ignited wildfires, they are typically tailored to specific regions with unique characteristics, limiting their global applicability. In this study, we present machine learning models designed to characterize and predict lightning-ignited wildfires on a global scale. Our approach involves classifying lightning-ignited versus anthropogenic wildfires, and estimating with high accuracy the probability of lightning to ignite a fire based on a wide spectrum of factors such as meteorological conditions and vegetation. Utilizing these models, we analyze seasonal and spatial trends in lightning-ignited wildfires shedding light on the impact of climate change on this phenomenon. We analyze the influence of various features on the models using eXplainable Artificial Intelligence (XAI) frameworks. Our findings highlight significant global differences between anthropogenic and lightning-ignited wildfires. Moreover, we demonstrate that, even over a short time span of less than a decade, climate changes have steadily increased the global risk of lightning-ignited wildfires. This distinction underscores the imperative need for dedicated predictive models and fire weather indices tailored specifically to each type of wildfire.
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