Abstract
Abstract. 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 (n=396/1125). 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 ∼60 % 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.
Highlights
This study examines the consistency of flag warnings at Pensacola Beach, Florida, between 2004 and 2008 when daily data are available for flag colour, wind, and wave forcing, as well as the daily number of rescues performed by lifeguards
The results suggest that the difference between the posted and predicted flag colour could be associated with the lifeguards noting that the nearshore had a transverse-bar and rip morphology, which is common at this location
A decision tree analysis predicts a flag colour different to the one flown on ∼ 35 % of days between 2004 and 2008 (n = 396/1125) and that those differences account for only 17 % of all rescue days and ∼ 60 % of the total number of rescues
Summary
Rip currents are the main hazard to recreational swimmers and bathers and, in recent years, have been recognized as a serious global public health issue (Brighton et al, 2013; Woodward et al, 2013; Kumar and Prasad, 2014; Arozarena et al, 2015; Brewster et al, 2019; Vlodarchyk et al, 2019). Seaward-directed currents that can develop on beaches characterized by wave breaking within the surf zone (Castelle et al, 2016) and are capable of transporting swimmers a significant distance away from the shoreline into deeper waters. Weak swimmers or those who try and fight the current can become stressed and experience panic (Brander et al, 2011; Drozdzewski et al, 2012), leading to increased adrenaline, an elevated heart rate and blood pressure, and rapid and shallow breathing. Recent evidence suggests that public knowledge of this hazard is limited
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