论文标题
野火传播建模的复发神经网络体系结构的比较
Comparison of Recurrent Neural Network Architectures for Wildfire Spread Modelling
论文作者
论文摘要
野火建模是重现火灾行为的尝试。通过主动火灾分析，可以在持续时间序列数据有限的情况下重现动态过程，例如野火。复发性神经网络（RNN）可以通过记住其内部输入的能力来对动态时间行为进行建模。在本文中，我们比较了封闭式复发单元（GRU）和长短期内存（LSTM）网络。我们试图确定野火是否继续燃烧并鉴于它确实如此，我们旨在预测野火将传播的8个主要方向中的哪一个。总体而言，GRU在更长的时间序列中的表现要比LSTM较长。我们已经表明，尽管我们在预测野火蔓延的方向方面是合理的，但由于缺乏辅助数据，野火是否继续燃烧，我们无法进行评估。
Wildfire modelling is an attempt to reproduce fire behaviour. Through active fire analysis, it is possible to reproduce a dynamical process, such as wildfires, with limited duration time series data. Recurrent neural networks (RNNs) can model dynamic temporal behaviour due to their ability to remember their internal input. In this paper, we compare the Gated Recurrent Unit (GRU) and the Long Short-Term Memory (LSTM) network. We try to determine whether a wildfire continues to burn and given that it does, we aim to predict which one of the 8 cardinal directions the wildfire will spread in. Overall the GRU performs better for longer time series than the LSTM. We have shown that although we are reasonable at predicting the direction in which the wildfire will spread, we are not able to asses if the wildfire continues to burn due to the lack of auxiliary data.
