Data-driven prediction and visualisation of dynamic bushfire risks

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

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationDatabases Theory and Applications
Subtitle of host publication27th Australasian Database Conference, ADC 2016, Sydney, NSW, September 28–29, 2016, Proceedings
EditorsMuhammad Aamir Cheema, Wenjie Zhang, Lijun Chang
Place of PublicationCham Switzerland
Number of pages5
ISBN (Electronic)9783319469225
ISBN (Print)9783319469218
Publication statusPublished - 2016
Externally publishedYes
EventAustralasian Database Conference 2016 - The University of New South Wales, Sydney, Australia
Duration: 28 Sep 201629 Sep 2016
Conference number: 27th (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceAustralasian Database Conference 2016
Abbreviated titleADC 2016
OtherThe Australasian Database Conference series is an annual forum for sharing the latest research progresses and novel applications of database systems, data driven applications and data analytics for researchers and practitioners in these areas from Australia, New Zealand and in the world.
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