An artificial intelligence framework for predicting fire spread sustainability in semiarid shrublands
Sadegh Khanmohammadi
A , Mehrdad Arashpour A * , Emadaldin Mohammadi Golafshani A , Miguel G. Cruz B and Abbas Rajabifard C
+ Author Affiliations
- Author Affiliations
A Department of Civil Engineering, Monash University, Melbourne, Australia.
B CSIRO, GPO Box 1700, Canberra, ACT 2601, Australia.
C School of Engineering, Melbourne University, Melbourne, Vic. 3000, Australia.
* Correspondence to: mehrdad.arashpour@monash.edu
International Journal of Wildland Fire 32(4) 636-649 https://doi.org/10.1071/WF22216
Submitted: 12 November 2022 Accepted: 14 January 2023 Published: 3 February 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF.
Abstract
Background: Fire behaviour simulation and prediction play a key role in supporting wildfire management and suppression activities.
Aims: Using machine-learning methods, the aim of this study was to predict the onset of fire propagation (go vs no-go) and type of fire behaviour (surface vs crown fire) in southern Australian semiarid shrublands.
Methods: Several machine-learning (ML) approaches were tested, including Support Vector Machine, Multinomial Naive Bayes and Multilayered Neural Networks, as was the use of augmented datasets developed with Generative Adversarial Networks (GAN) in classification of fire type.
Key results: Support Vector Machine was determined as the optimum machine learning classifier based on model overall accuracy against an independent evaluation dataset. This classifier correctly predicted fire spread sustainability and active crown fire propagation in 70 and 79% of the cases, respectively. The application of synthetically generated datasets in the Support Vector Machine model fitting process resulted in an improvement of model accuracy by 20% for the fire sustainability classification and 4% for the crown fire occurrence.
Conclusions: The selected ML modelling approach was shown to produce better results than logistic regression models when tested on independent datasets.
Implications: Artificial intelligence frameworks have a role in the development of predictive models of fire behaviour.
Keywords: artificial intelligence (AI), bushfire, climate change, feature selection, SHapley Additive exPlanations (SHAP), Stochastic Gradient Descent (SGD), Tabular Generative Adversarial Networks (TGAN), wildfire.
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