As wildfires are expected to become more frequent and severe, improved prediction models are vital to mitigating risk and allocating resources. With remote sensing data, valuable spatiotemporal statistical models can be created and used for resource management practices. In this paper, we create a dynamic model for future wildfire predictions of five locations within the western United States through a deep neural network via historical burned area and climate data. The proposed model has distinct features that address the characteristic need in prediction evaluations, including dynamic online estimation and time-series modeling. Between locations, local fire event triggers are not isolated, and there are confounding factors when local data is analyzed due to incomplete state observations. When compared to existing approaches that do not account for incomplete state observation within wildfire time-series data, on average, we are able to achieve higher prediction performances.
翻译:由于预计野火将变得更加频繁和严重,改进的预测模型对于减少风险和分配资源至关重要。遥感数据可以创造宝贵的时空统计模型,用于资源管理实践。在本文件中,我们通过历史燃烧地区和气候数据建立一个深层神经网络,为未来对美国西部五个地点的野火预测创造一个动态模型。拟议模型具有不同的特点,满足了预测评估的典型需要,包括动态在线估计和时间序列模型。在不同地点之间,当地火灾触发器不是孤立的,在对当地数据进行分析时,由于国家观测不完全,存在着一些令人困惑的因素。与现有方法相比,在野火时间序列数据中不完全观察状态,我们平均能够实现更高的预测性能。