Preview

PDF 471Kb

Unplanned fire is a major control on the nature of terrestrial ecosystems and causes substantial losses of life and property. Given the substantial influence of climatic conditions on fire incidence, climate change is expected to substantially change fire regimes in many parts of the world. We wished to determine whether it was possible to develop a deep neural network process for accurately estimating continental fire incidence from publicly available climate data. We show that deep recurrent Elman neural network was the best performed out of ten artificial neural networks (ANN) based cognitive imaging systems for determining the relationship between fire incidence and climate. In a decennium data experiment using this ANN we show that it is possible to develop highly accurate estimations of fire incidence from monthly climatic data surfaces. Our estimations for the continent of Australia had over 90% global accuracy and a very low level of false negatives. The technique is thus appropriate for use in estimating the spatial consequences of climate scenarios on the monthly incidence of wildfire at the landscape scale.

Item Type:	Refereed Article
Research Division:	Engineering
Research Group:	Geomatic engineering
Research Field:	Photogrammetry and remote sensing
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in the environmental sciences
UTAS Author:	Aryal, J (Dr Jagannath Aryal)
UTAS Author:	Das, A (Dr Aruneema Das)
UTAS Author:	Kirkpatrick, JB (Professor James Kirkpatrick)
ID Code:	87639
Year Published:	2013
Web of Science® Times Cited:	27
Deposited By:	Geography and Environmental Studies
Deposited On:	2013-11-28
Last Modified:	2014-10-21
Downloads:	307 View Download Statistics