论文标题
使用深神经网络对野火周边演变进行建模
Modeling Wildfire Perimeter Evolution using Deep Neural Networks
论文作者
论文摘要
随着野火事件的规模和频率的增加，对不断发展的野火前线的准确实时预测是消防努力和最佳管理实践的关键组成部分。我们提出了一种野火扩散模型，以预测24小时内野火周边的演变。火灾传播模拟基于深度卷积神经网络（CNN），该卷积神经网络（CNN）是训练有素的大气和环境时间SE-RIES数据。我们表明，该模型能够从加利福尼亚州西部内华达山脉的一系列野火中从真实的历史数据集中学习野火动态。我们在以前看不见的野火中验证该模型，并产生现实的结果，这些结果显着超过了良好的替代方案，其验证精度为78％-98％
With the increased size and frequency of wildfire eventsworldwide, accurate real-time prediction of evolving wildfirefronts is a crucial component of firefighting efforts and for-est management practices. We propose a wildfire spreadingmodel that predicts the evolution of the wildfire perimeter in24 hour periods. The fire spreading simulation is based ona deep convolutional neural network (CNN) that is trainedon remotely sensed atmospheric and environmental time se-ries data. We show that the model is able to learn wildfirespreading dynamics from real historic data sets from a seriesof wildfires in the Western Sierra Nevada Mountains in Cal-ifornia. We validate the model on a previously unseen wild-fire and produce realistic results that significantly outperformhistoric alternatives with validation accuracies ranging from78% - 98%
