Application of multiple linear regression and Bayesian belief network approaches to model life risk to beach users in the UK

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Stokes, Christopher and Masselink, Gerhard and Revie, Matthew and Scott, Timothy and Purves, David and Walters, Thomas (2017) Application of multiple linear regression and Bayesian belief network approaches to model life risk to beach users in the UK. Ocean and Coastal Management, 139. pp. 12-23. ISSN 0964-5691 (https://doi.org/10.1016/j.ocecoaman.2017.01.025)

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Abstract

A data-driven, risk-based approach is being pursued by the Royal National Lifeboat Institution (RNLI) to guide the selection of beaches for new lifeguard services around the UK coast. In this contribution, life risk to water users is quantified from the number and severity of life-threatening incidents at a beach during the peak summer tourist season, and this predictand is modelled using both multiple linear regression and Bayesian belief network approaches. First, the underlying levels of hazard and water-user exposure at each beach were quantified, and a dataset of 77 potential predictor variables was collated at 113 lifeguarded beaches. These data were used to develop exposure and hazard sub-models, and a final prediction of peak-season life risk wasmade at each beach from the product of the exposure and hazard predictions. Both the regression and Bayesian network algorithms identified that intermediate morphology is associated with increased hazard, while beaches with a slipway were predicted to be less hazardous than those without a slipway. Beaches with increased car parking area and beaches enclosed by headlands were associated with higher water-user numbers by both algorithms, and beach morphology type was seen to either increase water-user numbers (intermediate morphology in the regression model) or decrease water-user numbers (reflective morphology in the Bayesian network). Overall, intermediate beach morphology can be considered the most crucial contributor to water-user life risk, as it was linked to both higher hazard, and higher water-user exposure. The predictive skill of the regression and Bayesian network models are compared, and the benefits that each approach provides to beach risk managers are discussed.

ORCID iDs

Stokes, Christopher, Masselink, Gerhard, Revie, Matthew ORCID logoORCID: https://orcid.org/0000-0002-0130-8109, Scott, Timothy, Purves, David ORCID logoORCID: https://orcid.org/0000-0001-8294-0706 and Walters, Thomas;
  • Item type:Article
    ID code:59610
    Dates:
    Date
    Event
    1 April 2017
    Published
    2 February 2017
    Published Online
    24 January 2017
    Accepted
    26 May 2016
    Submitted
    Notes:© 2017 Elsevier Ltd. All rights reserved. Christopher Stokes, Gerhard Masselink, Matthew Revie, Timothy Scott, David Purves, Thomas Walters, Application of multiple linear regression and Bayesian belief network approaches to model life risk to beach users in the UK, Ocean & Coastal Management, Volume 139, 2017, Pages 12-23, https://doi.org/10.1016/j.ocecoaman.2017.01.025
    Subjects:Social Sciences > Industries. Land use. Labor
    Department:Strathclyde Business School > Management Science
    Strategic Research Themes > Innovation Entrepreneurship
    Depositing user: Pure Administrator
    Date deposited:26 Jan 2017 16:12
    Last modified:11 Nov 2024 11:37
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/59610
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