Exploratory analysis of driving force of wildfires in australia: An application of machine learning within google earth engine

Andrea Sulova, Jamal Jokar Arsanjani*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

19 Citations (Scopus)
18 Downloads (Pure)

Abstract

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

Original languageEnglish
Article number10
JournalRemote Sensing
Volume13
Issue number1
Pages (from-to)1-23
Number of pages23
ISSN2072-4292
DOIs
Publication statusPublished - 1 Jan 2021

Bibliographical note

Publisher Copyright:
© 2020 by the authors. Li-censee MDPI, Basel, Switzerland.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Classification and Regression Tree
  • Fire severity
  • Google Earth Engine
  • Machine learning
  • Naïve Bayes
  • Random forest
  • Remote sensing
  • Sustainable Development Goals
  • Wildfires

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