Citation

Dutta R, Das A, Aryal J. R. Soc. Open Sci. 2016; 3(2): 150241.

Affiliation

School of Land and Food, University of Tasmania , Hobart, Tasmania 7001, Australia.

Copyright

(Copyright © 2016, Royal Society Publishing)

DOI

10.1098/rsos.150241

PMID

26998312

PMCID

PMC4785963

Abstract

Increasing Australian bush-fire frequencies over the last decade has indicated a major climatic change in coming future. Understanding such climatic change for Australian bush-fire is limited and there is an urgent need of scientific research, which is capable enough to contribute to Australian society. Frequency of bush-fire carries information on spatial, temporal and climatic aspects of bush-fire events and provides contextual information to model various climate data for accurately predicting future bush-fire hot spots. In this study, we develop an ensemble method based on a two-layered machine learning model to establish relationship between fire incidence and climatic data. In a 336 week data trial, we demonstrate that the model provides highly accurate bush-fire incidence hot-spot estimation (91% global accuracy) from the weekly climatic surfaces. Our analysis also indicates that Australian weekly bush-fire frequencies increased by 40% over the last 5 years, particularly during summer months, implicating a serious climatic shift.

Language: en