Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics

As the climate changes, the severity of wildland fires is expected to worsen. Understanding, controlling and mitigating these fires requires building models to accurately capture the fire-propagation dynamics. Supervised machine learning techniques provide a potential approach for developing such models. The objective of this study is to evaluate the feasibility of using the Convolutional Long Short-Term Memory (ConvLSTM) recurrent neural network (RNN) to model the dynamics of wildland fire propagation. The model is trained on simulated wildfire data generated by a cellular automaton percolation model. Four 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 realistic vegetation density distributions. We examine how effectively the ConvLSTM can capture the fire-spread dynamics over consecutive time steps using classification and regression metrics. It is shown that these ConvLSTMs are capable of capturing 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 non-temporal Convolutional Neural Networks(CNNs), particularly on the most difficult dataset.