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Data-Driven Prediction and Visualisation of Dynamic Bushfire Risks

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Databases Theory and Applications (ADC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9877))

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Abstract

The potential impact of bushfires is a significant concern for communities and fire response agencies, and the ability to predict the fire risk timely and accurately is critical. However, that cannot be achieved without accessing and processing very large amounts of data in almost real time. We demonstrate a data-driven fire risk prediction system that leverages big geospatial and meteorological data, where the results are visualised and made available to communities and fire agencies for risk mitigation strategies.

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References

  1. NSW Rural Fire Service: BAL risk assessment application kit. New dwellings and alterations and additions to existing dwellings (2012)

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  2. McArthur, A.G.: Fire behaviour in eucalypt forests. Comm. of Australia For. & Timber Bur. Leaflet No. 107 (1967)

    Google Scholar 

  3. Dee, D., Uppala, S., Simmons, A., Berrisford, P., et al.: The era-interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137(6565), 553–597 (2011)

    Article  Google Scholar 

  4. Salehi, M., Rusu, L.I., Lynar, T., Phan, A.: Dynamic and robust wildfire risk prediction system: an unsupervised approach. In: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD) (2016)

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  5. Wang, Z., Roberts, M.E., Rusu, L.I.: Dynamic and personalised recommendations for proactive bushfire risk management. In: Australian and New Zealand Disaster and Emergency Management Conference (ANZDMC), pp. 207–222 (2016)

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Author information

Authors and Affiliations

  1. IBM Research, 204 Lygon Street, Carlton, VIC, 3053, Australia

    Laura Rusu, Hoang Tam Vo, Ziyuan Wang, Mahsa Salehi & Anna Phan

Authors
  1. Laura Rusu
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  2. Hoang Tam Vo
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  3. Ziyuan Wang
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  4. Mahsa Salehi
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  5. Anna Phan
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Corresponding author

Correspondence to Laura Rusu .

Editor information

Editors and Affiliations

  1. Monash University , Clayton, Australia

    Muhammad Aamir Cheema

  2. School of Comp. Science a. Engineer, University of New South Wales School of Comp. Science a. Engineer, Sydney, Australia

    Wenjie Zhang

  3. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

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© 2016 Springer International Publishing AG

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Cite this paper

Rusu, L., Vo, H.T., Wang, Z., Salehi, M., Phan, A. (2016). Data-Driven Prediction and Visualisation of Dynamic Bushfire Risks. In: Cheema, M., Zhang, W., Chang, L. (eds) Databases Theory and Applications. ADC 2016. Lecture Notes in Computer Science(), vol 9877. Springer, Cham. https://doi.org/10.1007/978-3-319-46922-5_39

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  • DOI: https://doi.org/10.1007/978-3-319-46922-5_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46921-8

  • Online ISBN: 978-3-319-46922-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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