@article{duttaDeepCognitiveImaging2013,
  title = {Deep Cognitive Imaging Systems Enable Estimation of Continental-Scale Fire Incidence from Climate Data},
  author = {Dutta, Ritaban and Aryal, Jagannath and Das, Aruneema and Kirkpatrick, Jamie B.},
  date = {2013-11},
  journaltitle = {Scientific Reports},
  volume = {3},
  issn = {2045-2322},
  doi = {10.1038/srep03188},
  url = {http://mfkp.org/INRMM/article/14073776},
  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.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14073776,~to-add-doi-URL,artificial-neural-networks,australia,deep-machine-learning,disasters,disturbances,forest-resources,soil-moisture,solar-radiation,wildfires,wind}
}

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B."],"bibdata":{"bibtype":"article","type":"article","title":"Deep Cognitive Imaging Systems Enable Estimation of Continental-Scale Fire Incidence from Climate Data","author":[{"propositions":[],"lastnames":["Dutta"],"firstnames":["Ritaban"],"suffixes":[]},{"propositions":[],"lastnames":["Aryal"],"firstnames":["Jagannath"],"suffixes":[]},{"propositions":[],"lastnames":["Das"],"firstnames":["Aruneema"],"suffixes":[]},{"propositions":[],"lastnames":["Kirkpatrick"],"firstnames":["Jamie","B."],"suffixes":[]}],"date":"2013-11","journaltitle":"Scientific Reports","volume":"3","issn":"2045-2322","doi":"10.1038/srep03188","url":"http://mfkp.org/INRMM/article/14073776","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. 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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. 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