论文标题
卷积LSTM神经网络，用于建模Wildland Fire Dynamils
Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics
论文作者
论文摘要
随着气候的变化，荒野大火的严重程度预计会恶化。准确捕获火灾传播动态的模型极大地帮助理解，响应和减轻这些火灾造成的损害的努力。机器学习技术为开发此类模型提供了一种潜在的方法。这项研究的目的是评估使用卷积长的短期记忆（ConvlstM）复发性神经网络的可行性，以模拟Wildland火灾传播的动力学。对机器学习模型进行了数学模型生成的模拟野火数据培训。分析了三个模拟数据集，每个数据集的复杂性程度越高。最简单的数据集包括一个恒定的风向作为单个混杂因素，而最复杂的数据集包括动态风，复杂的地形，空间变化的水分含量和异质植被密度分布。我们检查了弯路卷动员可以在连续的时间步长中学习开发动态的有效性。结果表明，Convlstms可以捕获当地的火传输事件以及整体火灾动态，例如大火传播的速度。最后，我们证明，ConvlSTMS优于以前用于建模类似Wildland Fire Dynamics的其他网络体系结构。
As the climate changes, the severity of wildland fires is expected to worsen. Models that accurately capture fire propagation dynamics greatly help efforts for understanding, responding to and mitigating the damages caused by these fires. Machine learning techniques provide a potential approach for developing such models. The objective of this study is to evaluate the feasibility of using a Convolutional Long Short-Term Memory (ConvLSTM) recurrent neural network to model the dynamics of wildland fire propagation. The machine learning model is trained on simulated wildfire data generated by a mathematical analogue model. Three simulated datasets are analyzed, each with increasing degrees of complexity. The simplest dataset includes a constant wind direction as a single confounding factor, whereas the most complex dataset includes dynamic wind, complex terrain, spatially varying moisture content and heterogenous vegetation density distributions. We examine how effective the ConvLSTM can learn the fire-spread dynamics over consecutive time steps. It is shown that ConvLSTMs can capture local fire transmission events, as well as the overall fire dynamics, such as the rate at which the fire spreads. Finally, we demonstrate that ConvLSTMs outperform other network architectures that have previously been used to model similar wildland fire dynamics.
