Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States. Many efforts have been made to understand the causes of the increases in wildfire intensity and frequency in recent years. In this work, we propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California by utilizing a multitude of environmental data including population density, Normalized Difference Vegetation Index (NDVI), Palmer Drought Severity Index (PDSI), tree mortality area, tree mortality number, and altitude. Moreover, we focus on a better understanding of the impacts of different factors so as to inform preventive actions. To validate our models and findings, we divide the land of California into 4,242 grids of 0.1 degrees $\times$ 0.1 degrees in latitude and longitude, and compute the risk of each grid based on spatial and temporal conditions. To verify the generalizability of our models, we further expand the scope of wildfire risk assessment from California to Washington without any fine tuning. By performing counterfactual analysis, we uncover the effects of several possible methods on reducing the number of high risk wildfires. Taken together, our study has the potential to estimate, monitor, and reduce the risks of wildfires across diverse areas provided that such environment data is available.
翻译:野火是美国西海岸经常发生的最大灾害之一。许多努力都是为了了解近年来野火强度和频率增加的原因。在这项工作中,我们提出静态和动态的预测模型,以便分析和评估加利福尼亚地区野火风险高的地区,方法是利用大量环境数据,包括人口密度、标准化差异植被指数(NDVI)、帕尔默干旱严重性指数(PDSI)、树木死亡率地区、树木死亡率和高度。此外,我们侧重于更好地了解不同因素的影响,以便告知预防行动。为了验证我们的模型和发现,我们把加利福尼亚州的土地分为4 242个纬度和经度0.1度的0.1度的电网,并根据空间和时间条件计算每个电网的风险。为了核实我们模型的可概括性,我们进一步将野火风险评估的范围从加利福尼亚扩大到华盛顿,而不作任何微调。通过反事实分析,我们发现几种可能的方法对减少高风险野火数量的影响。我们的研究将加利福尼亚州的土地分成4 4 242个纬度和经度0.1度的电网,并根据空间和时间条件对每个电网的风险进行计算。我们的研究能够估计、监测、提供这种火的可能性。