Almost 250 people drowned across Australia in 2018. A spate of tragic incidents on NSW Beaches in early 2019 again highlighted the need for increased beach safety and awareness of dangerous conditions. Currently, there is inconsistency in the nature of beach data collection along Australia’s coast, with different councils collecting different information relating to crowd numbers, activity and localised conditions. Collection and recording of beach usage information is a time-consuming and imprecise task for professional lifeguards.
The Smart Beaches project is an initiative of Lake Macquarie City Council in partnership with Northern Beaches Council and the University of Technology Sydney. The project received $910,000 in funding under Round Two of the Australian Government’s Smart Cities and Suburbs Program. The project aimed to use technology to support lifeguard risk assessment and management, allowing them to focus on their primary role of protecting public safety.
From the outset the project adopted a collaborative approach evidenced by the over 16 partners and even more service providers ranging from coastal experts, technology providers, analysis, smart cities specialists, artists, entrepreneurs and tradespeople. We held extensive workshops with the lifeguards to understand their needs and the challenges of their role. We consulted widely and attended a presented at a number of industry forums.
Many of the factors for consideration as part of a risk management process for beaches can be measured and automated using technology including:
Throughout the project we trialled a range of sensors including cameras, local weather stations, lifeguard monitors, GPS trackers, amenity sensors, bin sensors, water flow sensors. A range of analytical methods were used to gather hourly crowd counts from the camera data and the GPS trackers were used to monitor flag locations and rescue equipment deployment. The project also sourced a range of extant data such as weather, and lifeguard reports and worked with Manly Hydraulics Lab to make available their Nearshore Wave Transformation Toolbox which closely models nearshore conditions based on off shore buoy data.
The project sponsored a range of research activities including a summary of technology currently employed at beaches; a data ontology study to understand and improve the terminology used in the field; and a study into the relationship between tide state and fatal drownings. We have also collaborated with other related research project globally.
We also trialled a number of new and innovative communication techniques. With Ocean Live we sponsored Dean and Jackson Harrigan to continue development of their App through to first release (available on the Apple store). The app provides a traffic light warning system to the public in a similar vein to those seen at ski fields and bushfire warning signs. The app also includes the ability for lifeguards to login, adjust any warnings and send out customised messages to the public. We also trialled their flashing rip warning sign, allowing lifeguards to highlight particularly dangerous rips and guide beachgoers to the flags. With Safe2Swim we supported Deb Keys in her experiment to trial QR codes to guide beachgoers to multilingual information. With Metromatics we have developed a “digital chalkboard” that can display a range of live beach data in place of the traditional lifeguard noticeboard. The Digital Chalkboard uses a unique digital paper colour display that is lightweight, compact and requires extremely low power usage. The display content is fully customisable leaving ample scope for future development and innovation.
The outcome is a well developed and tested framework to deliver immediate value to our lifeguards on the beaches and to support medium to long-term planning for beach safety management. We have determined the parts of the system that are most useful, reliable and available and how these can be presented to best support lifeguards and their managers. We also have the beginnings of a system to not only present this data but to use it to summarise risk levels and predict future scenarios.