Comparison of Recurrent Neural Network Architectures for Wildfire Spread Modelling

Rylan Perumal, Terence L.Van Zyl

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2020 International SAUPEC/RobMech/PRASA Conference, SAUPEC/RobMech/PRASA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728141626
DOIs
Publication statusPublished - Jan 2020
Externally publishedYes
Event2020 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa, SAUPEC/RobMech/PRASA 2020 - Cape Town, South Africa
Duration: 29 Jan 202031 Jan 2020

Publication series

Name2020 International SAUPEC/RobMech/PRASA Conference, SAUPEC/RobMech/PRASA 2020
Volume2020-January

Conference

Conference2020 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa, SAUPEC/RobMech/PRASA 2020
Country/TerritorySouth Africa
CityCape Town
Period29/01/2031/01/20

Keywords

  • machine learning
  • recurrent neural networks
  • wildfire spread modelling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Energy Engineering and Power Technology
  • Mechanical Engineering

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