Surf zone injury study along the Delaware Atlantic-fronting coast: quantification, prediction, and directed awareness
Date
2018
Authors
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Publisher
University of Delaware
Abstract
Surf zone injury (SZI) data were collected along the ~24 miles of Atlanticfronting Delaware coast for eight summer seasons from 2010 to 2017 to quantify injuries, predict injuries, and direct an awareness campaign. Beebe Healthcare in Lewes, Delaware collected SZI data, including but not limited to time of injury, gender, age, and activity. SZI occurring at the five most populated beaches exceeded 2000 and included six fatalities. The relative demographics of the injured population are similar despite fluctuating injury totals (mean [standard deviation (SD)], 253.1 [104.4]). Non-locals (n = 1757) were 6.7 times more likely to be injured as their local (n =264) counterparts (relative risk (RR), 2.62; 95% CI, 2.08 - 3.31). Males (n = 1258) were 1.7 times more likely to be injured than their female (n = 763) counterparts (RR, 1.29; 95% CI, 1.21 - 1.37). Serious injuries, defined as patients requiring admission to a trauma service, represented 9.1% (n = 184) of injuries. Wading (50.1%) was found to be the dominant activity associated with injury followed by body surfing (18.4%), and body boarding (13.3%). Beachgoer questionnaires suggest knowledge for nonlocals is lacking as only 16.6% of those surveyed had seen information about SZI or shore break warnings. ☐ Of the eight summers of data collection, 32.9% of days there were no injuries and 3.6% of days there were more than 10 injuries (max = 24 injuries in one day). The episodic nature of SZI indicate the importance of linking the environmental conditions and human behavior in the surf to predict days with high injury rates. Higher order statistics are necessary to effectively consider all associated factors related to SZI. Two Bayesian networks (BN) using Netica software (Norsys, Netica v. 6.03, www.norsys.com) were constructed to model SZI and predict changes in injury rate and likelihood on an hourly basis. The models incorporate environmental data collected by weather stations, wave gauges, and researcher personnel on the beach. The models include prior (e.g., historic) information to infer relationships between provided parameters. Sensitivity analysis determined the most influential parameters related to injury rates were significant wave height, foreshore slope, and water temperature. Exposure parameters (e.g., air temperature) influenced the number of people in the water, resulting in strong correlation between injury likelihood and the related meteorological conditions (variance reduction > 0.4%). Log likelihood ratio (LLR) scores indicate the network predicts SZI likelihood with more skill than prior predictions with the best performing model improving prediction 69.1% of the time. When all parameters were included, the BN set up as a binary problem predicting injury likelihood during an hour performed better (positive LLR = 69.1%) than the BN predicting injury rate (positive LLR = 36.7%). Issues persist with predicting SZI that have an LLR ≪ -1 (< 5% of 2017 injuries) and occur in conditions different than when most other SZI occur. Better understanding of SZI will improve awareness techniques to both educate beachgoers and assist beach patrol decision making during high risk conditions.
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Keywords
Applied sciences, Bayesian network, Beach, Hazard, Surf zone injuries, Water user