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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Rethinking data-driven decision support in flood risk management for a big data age
AU - Towe, Ross
AU - Dean, Graham
AU - Edwards, Elizabeth
AU - Nundloll, Vatsala
AU - Blair, Gordon
AU - Lamb, Rob
AU - Hankin, Barry
AU - Manson, Susan
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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.
KW - big data
KW - cloud computing
KW - data hypercube
KW - data science
KW - flexible querying
KW - semantic web
KW - uncertainty
U2 - 10.1111/jfr3.12652
DO - 10.1111/jfr3.12652
M3 - Journal article
VL - 13
JO - Journal of Flood Risk Management
JF - Journal of Flood Risk Management
SN - 1753-318X
IS - 4
M1 - e12652
ER -