TY - GEN
T1 - Comparison of Recurrent Neural Network Architectures for Wildfire Spread Modelling
AU - Perumal, Rylan
AU - Zyl, Terence L.Van
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/1
Y1 - 2020/1
N2 - 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.
AB - 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.
KW - machine learning
KW - recurrent neural networks
KW - wildfire spread modelling
UR - http://www.scopus.com/inward/record.url?scp=85084632888&partnerID=8YFLogxK
U2 - 10.1109/SAUPEC/RobMech/PRASA48453.2020.9078028
DO - 10.1109/SAUPEC/RobMech/PRASA48453.2020.9078028
M3 - Conference contribution
AN - SCOPUS:85084632888
T3 - 2020 International SAUPEC/RobMech/PRASA Conference, SAUPEC/RobMech/PRASA 2020
BT - 2020 International SAUPEC/RobMech/PRASA Conference, SAUPEC/RobMech/PRASA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa, SAUPEC/RobMech/PRASA 2020
Y2 - 29 January 2020 through 31 January 2020
ER -