Rethinking data‐driven decision support in flood risk management for a big data age

Towe, Ross; Dean, Graham ORCID: https://orcid.org/0000-0002-7129-1875; Edwards, Liz; Nundloll, Vatsala; Blair, Gordon; Lamb, Rob; Hankin, Barry; Manson, Susan. 2020 Rethinking data‐driven decision support in flood risk management for a big data age. Journal of Flood Risk Management, 13 (4), e12652. 19, pp. https://doi.org/10.1111/jfr3.12652

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Abstract/Summary

Decision‐making in flood risk management is increasingly dependent on access to data, with the availability of data increasing dramatically in recent years. We are therefore moving towards an era of big data, with the added challenges that, in this area, data sources are highly heterogeneous, at a variety of scales, and include a mix of structured and unstructured data. The key requirement is therefore one of integration and subsequent analyses of this complex web of data. This paper examines the potential of a data‐driven approach to support decision‐making in flood risk management, with the goal of investigating a suitable software architecture and associated set of techniques to support a more data‐centric approach. The key contribution of the paper is a cloud‐based data hypercube that achieves the desired level of integration of highly complex data. This hypercube builds on innovations in cloud services for data storage, semantic enrichment and querying, and also features the use of notebook technologies to support open and collaborative scenario analyses in support of decision making. The paper also highlights the success of our agile methodology in weaving together cross‐disciplinary perspectives and in engaging a wide range of stakeholders in exploring possible technological futures for flood risk management.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1111/jfr3.12652
UKCEH and CEH Sections/Science Areas:	Pollution (Science Area 2017-)
ISSN:	1753-318X
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	big data, cloud computing, data hypercube, data science, flexible querying, semantic web, uncertainty
NORA Subject Terms:	Hydrology Data and Information
Date made live:	26 Nov 2020 15:58 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/529033

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