  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.
