Gene expression programming and data mining methods for bushfire susceptibility mapping in New South Wales, Australia

Maryamsadat Hosseini () and Samsung Lim
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Maryamsadat Hosseini: University of New South Wales
Samsung Lim: University of New South Wales

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 113, issue 2, No 22, 1349-1365

Abstract: Abstract Australia is one of the most bushfire-prone countries. Prediction and management of bushfires in bushfire-susceptible areas can reduce the negative impacts of bushfires. The generation of bushfire susceptibility maps can help improve the prediction of bushfires. The main aim of this study was to use single gene expression programming (GEP) and ensemble of GEP with well-known data mining to generate bushfire susceptibility maps for New South Wales, Australia, as a case study. We used eight methods for bushfire susceptibility mapping: GEP, random forest (RF), support vector machine (SVM), frequency ratio (FR), ensemble techniques of GEP and FR (GEPFR), RF and FR (RFFR), SVM and FR (SVMFR), and logistic regression (LR) and FR (LRFR). Areas under the curve (AUCs) of the receiver operating characteristic were used to evaluate the proposed methods. GEPFR exhibited the best performance for bushfire susceptibility mapping based on the AUC (0.892 for training, 0.890 for testing), while RFFR had the highest accuracy (95.29% for training, 94.70% for testing) among the proposed methods. GEPFR is an ensemble method that uses features from the evolutionary algorithm and the statistical FR method, which results in a better AUC for the bushfire susceptibility maps. Single GEP showed AUC of 0.884 for training and 0.882 for testing. RF also showed AUC of 0.902 and 0.876 for training and testing, respectively. SVM had 0.868 for training and 0.781 for testing for bushfire susceptibility mapping. The ensemble methods had better performances than those of the single methods.

Keywords: Gene expression programming; Bushfire; Susceptibility map; Machine learning (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-022-05350-7

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