eCite Digital Repository

Deep cognitive imaging systems enable estimation of continental-scale fire incidence from climate data

Citation

Dutta, R and Aryal, J and Das, A and Kirkpatrick, JB, Deep cognitive imaging systems enable estimation of continental-scale fire incidence from climate data, Scientific Reports, 3 Article 3188. ISSN 2045-2322 (2013) [Refereed Article]


Preview
PDF
471Kb
  

Copyright Statement

Copyright 2013 Macmillan Publishers Licensed under Creative Commons Attribution NonCommercial-NoDerivs 3.0 Unported license http://creativecommons.org/licenses/by-nc-nd/3.0

DOI: doi:10.1038/srep03188

Abstract

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 Details

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
Author:Aryal, J (Dr Jagannath Aryal)
Author:Das, A (Dr Aruneema Das)
Author:Kirkpatrick, JB (Professor James Kirkpatrick)
ID Code:87639
Year Published:2013
Web of Science® Times Cited:9
Deposited By:Geography and Environmental Studies
Deposited On:2013-11-28
Last Modified:2014-10-21
Downloads:199 View Download Statistics

Repository Staff Only: item control page