Data-driven prediction and visualisation of dynamic bushfire risks

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

doi:10.1007/978-3-319-46922-5_39

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 proceeding › Conference Paper › Other

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Databases Theory and Applications
Subtitle of host publication	27th Australasian Database Conference, ADC 2016, Sydney, NSW, September 28–29, 2016, Proceedings
Editors	Muhammad Aamir Cheema, Wenjie Zhang, Lijun Chang
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	457-461
Number of pages	5
ISBN (Electronic)	9783319469225
ISBN (Print)	9783319469218
DOIs	https://doi.org/10.1007/978-3-319-46922-5_39
Publication status	Published - 2016
Externally published	Yes
Event	Australasian Database Conference 2016 - The University of New South Wales, Sydney, Australia Duration: 28 Sep 2016 → 29 Sep 2016 Conference number: 27th https://adc2016.cse.unsw.edu.au/ https://link.springer.com/book/10.1007/978-3-319-46922-5 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	9877
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Australasian Database Conference 2016
Abbreviated title	ADC 2016
Country	Australia
City	Sydney
Period	28/09/16 → 29/09/16
Other	The 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.
Internet address	https://adc2016.cse.unsw.edu.au/ https://link.springer.com/book/10.1007/978-3-319-46922-5 (Proceedings)

Access to Document

10.1007/978-3-319-46922-5_39

Link to publication in Scopus

Cite this

Rusu, L., Vo, H. T., Wang, Z., Salehi, M., & Phan, A. (2016). Data-driven prediction and visualisation of dynamic bushfire risks. In M. Aamir Cheema, W. Zhang, & L. Chang (Eds.), Databases Theory and Applications: 27th Australasian Database Conference, ADC 2016, Sydney, NSW, September 28–29, 2016, Proceedings (pp. 457-461). (Lecture Notes in Computer Science ; Vol. 9877 ). Springer. https://doi.org/10.1007/978-3-319-46922-5_39

Rusu, Laura ; Vo, Hoang Tam ; Wang, Ziyuan ; Salehi, Mahsa ; Phan, Anna. / Data-driven prediction and visualisation of dynamic bushfire risks. Databases Theory and Applications: 27th Australasian Database Conference, ADC 2016, Sydney, NSW, September 28–29, 2016, Proceedings. editor / Muhammad Aamir Cheema ; Wenjie Zhang ; Lijun Chang. Cham Switzerland : Springer, 2016. pp. 457-461 (Lecture Notes in Computer Science ).

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title = "Data-driven prediction and visualisation of dynamic bushfire risks",

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.",

author = "Laura Rusu and Vo, {Hoang Tam} and Ziyuan Wang and Mahsa Salehi and Anna Phan",

year = "2016",

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pages = "457--461",

editor = "{Aamir Cheema}, Muhammad and Zhang, {Wenjie } and Chang, {Lijun }",

booktitle = "Databases Theory and Applications",

note = "Australasian Database Conference 2016, ADC 2016 ; Conference date: 28-09-2016 Through 29-09-2016",

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Rusu, L, Vo, HT, Wang, Z, Salehi, M & Phan, A 2016, Data-driven prediction and visualisation of dynamic bushfire risks. in M Aamir Cheema, W Zhang & L Chang (eds), Databases Theory and Applications: 27th Australasian Database Conference, ADC 2016, Sydney, NSW, September 28–29, 2016, Proceedings. Lecture Notes in Computer Science , vol. 9877 , Springer, Cham Switzerland , pp. 457-461, Australasian Database Conference 2016, Sydney, Australia, 28/09/16. https://doi.org/10.1007/978-3-319-46922-5_39

Data-driven prediction and visualisation of dynamic bushfire risks. / Rusu, Laura ; Vo, Hoang Tam; Wang, Ziyuan; Salehi, Mahsa; Phan, Anna.

Databases Theory and Applications: 27th Australasian Database Conference, ADC 2016, Sydney, NSW, September 28–29, 2016, Proceedings. ed. / Muhammad Aamir Cheema; Wenjie Zhang; Lijun Chang. Cham Switzerland : Springer, 2016. p. 457-461 (Lecture Notes in Computer Science ; Vol. 9877 ).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

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AB - 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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Rusu L, Vo HT, Wang Z, Salehi M, Phan A. Data-driven prediction and visualisation of dynamic bushfire risks. In Aamir Cheema M, Zhang W, Chang L, editors, Databases Theory and Applications: 27th Australasian Database Conference, ADC 2016, Sydney, NSW, September 28–29, 2016, Proceedings. Cham Switzerland : Springer. 2016. p. 457-461. (Lecture Notes in Computer Science ). https://doi.org/10.1007/978-3-319-46922-5_39