Machine learning analysis of lifeguard flag decisions and recorded rescues
<p>Rip currents and other surf hazards are an emerging public health issue globally. Lifeguards, warning flags, and signs are important, and to varying degrees they are effective strategies to minimize risk to beach users. In the United States and other jurisdictions around the world, lifeguar...
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doaj-e29802046ca2418dada92738472a32fe2020-11-25T01:32:26ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812019-11-01192541254910.5194/nhess-19-2541-2019Machine learning analysis of lifeguard flag decisions and recorded rescuesC. HouserJ. LehnerN. CherryP. Wernette<p>Rip currents and other surf hazards are an emerging public health issue globally. Lifeguards, warning flags, and signs are important, and to varying degrees they are effective strategies to minimize risk to beach users. In the United States and other jurisdictions around the world, lifeguards use coloured flags (green, yellow, and red) to indicate whether the danger posed by the surf and rip hazard is low, moderate, or high respectively. The choice of flag depends on the lifeguard(s) monitoring the changing surf conditions along the beach and over the course of the day using both regional surf forecasts and careful observation. There is a potential that the chosen flag is not consistent with the beach user perception of the risk, which may increase the potential for rescues or drownings. In this study, machine learning is used to determine the potential for error in the flags used at Pensacola Beach and the impact of that error on the number of rescues. Results of a decision tree analysis indicate that the colour flag chosen by the lifeguards was different from what the model predicted for 35 % of days between 2004 and 2008 (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M1" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>n</mi><mo>=</mo><mn mathvariant="normal">396</mn><mo>/</mo><mn mathvariant="normal">1125</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="70pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="c4f86db8dda04d2946260b962f129ea4"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="nhess-19-2541-2019-ie00001.svg" width="70pt" height="14pt" src="nhess-19-2541-2019-ie00001.png"/></svg:svg></span></span>). Days when there is a difference between the predicted and posted flag colour represent only 17 % of all rescue days, but those days are associated with <span class="inline-formula">∼60</span> % of all rescues between 2004 and 2008. Further analysis reveals that the largest number of rescue days and total number of rescues are associated with days where the flag deployed over-estimated the surf and hazard risk, such as a red or yellow flag flying when the model predicted a green flag would be more appropriate based on the wind and wave forcing alone. While it is possible that the lifeguards were overly cautious, it is argued that they most likely identified a rip forced by a transverse-bar and rip morphology common at the study site. Regardless, the results suggest that beach users may be discounting lifeguard warnings if the flag colour is not consistent with how they perceive the surf hazard or the regional forecast. Results suggest that machine learning techniques have the potential to support lifeguards and thereby reduce the number of rescues and drownings.</p>https://www.nat-hazards-earth-syst-sci.net/19/2541/2019/nhess-19-2541-2019.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
C. Houser J. Lehner N. Cherry P. Wernette |
spellingShingle |
C. Houser J. Lehner N. Cherry P. Wernette Machine learning analysis of lifeguard flag decisions and recorded rescues Natural Hazards and Earth System Sciences |
author_facet |
C. Houser J. Lehner N. Cherry P. Wernette |
author_sort |
C. Houser |
title |
Machine learning analysis of lifeguard flag decisions and recorded rescues |
title_short |
Machine learning analysis of lifeguard flag decisions and recorded rescues |
title_full |
Machine learning analysis of lifeguard flag decisions and recorded rescues |
title_fullStr |
Machine learning analysis of lifeguard flag decisions and recorded rescues |
title_full_unstemmed |
Machine learning analysis of lifeguard flag decisions and recorded rescues |
title_sort |
machine learning analysis of lifeguard flag decisions and recorded rescues |
publisher |
Copernicus Publications |
series |
Natural Hazards and Earth System Sciences |
issn |
1561-8633 1684-9981 |
publishDate |
2019-11-01 |
description |
<p>Rip currents and other surf hazards are an emerging public health issue
globally. Lifeguards, warning flags, and signs are important, and to varying
degrees they are effective strategies to minimize risk to beach users. In
the United States and other jurisdictions around the world, lifeguards use
coloured flags (green, yellow, and red) to indicate whether the danger posed
by the surf and rip hazard is low, moderate, or high respectively. The
choice of flag depends on the lifeguard(s) monitoring the changing surf
conditions along the beach and over the course of the day using both
regional surf forecasts and careful observation. There is a potential that
the chosen flag is not consistent with the beach user perception of the
risk, which may increase the potential for rescues or drownings. In this
study, machine learning is used to determine the potential for error in the
flags used at Pensacola Beach and the impact of that error on the number of
rescues. Results of a decision tree analysis indicate that the colour flag
chosen by the lifeguards was different from what the model predicted for
35 % of days between 2004 and 2008 (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M1" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>n</mi><mo>=</mo><mn mathvariant="normal">396</mn><mo>/</mo><mn mathvariant="normal">1125</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="70pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="c4f86db8dda04d2946260b962f129ea4"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="nhess-19-2541-2019-ie00001.svg" width="70pt" height="14pt" src="nhess-19-2541-2019-ie00001.png"/></svg:svg></span></span>). Days when there is a
difference between the predicted and posted flag colour represent only
17 % of all rescue days, but those days are associated with
<span class="inline-formula">∼60</span> % of all rescues between 2004 and 2008. Further analysis reveals that the largest number of rescue days and total number of rescues are associated with days where the flag deployed over-estimated the surf and hazard risk, such as a red or yellow flag flying when the model predicted a green flag would be more appropriate based on the wind and wave forcing alone. While it is possible that the lifeguards were overly cautious, it is argued that they most likely identified a rip forced by a transverse-bar and rip morphology common at the study site. Regardless, the results suggest that beach users may be discounting lifeguard warnings if the flag colour is not consistent with how they perceive the surf hazard or the regional forecast. Results suggest that machine learning techniques have the potential to support lifeguards and thereby reduce the number of rescues and drownings.</p> |
url |
https://www.nat-hazards-earth-syst-sci.net/19/2541/2019/nhess-19-2541-2019.pdf |
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