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Abstract <p>Rip currents pose a significant threat to beach safety, causing numerous drownings annually. This study integrates video monitoring (Video Beach Monitoring System – VBMS and Smartphone Beach Monitoring System – SBMS), satellite imagery, NavIC-based drifters, XBeach modelling, and YOLO-V5 Artificial Intelligence technology to enhance rip current detection and management at two rip-prone beaches: Rushikonda and RK Beaches, Visakhapatnam on east coast of India. We mapped 231 rip channels using high-resolution satellite data (2015–2017) and identified seasonal patterns via video analysis (2022–2023). Drifters validated rip velocities, while XBeach simulations (with RMSE 0.12&#xa0;m/s) replicated dynamics. The AI model achieved &gt;77% detection accuracy, reducing misses by combining video and satellite data. The Safe Beach portal delivers daily rip current forecasts for 175 Indian beaches using a statistical model, improving public safety. This approach, tested on Goa beaches (with ~70% AI accuracy), offers a scalable framework for global replication, enhancing tourism and reducing drowning risks; however, challenges remain in resource-limited regions.</p> Research highlights <p>Integrated video monitoring, satellite imagery, and AI technology effectively identified persistent rip channels at Rushikonda and RK Beaches, enhancing coastal hazard detection.<UnorderedList Mark="Bullet"> <ItemContent> <p>XBeach modelling, combined with NavIC drifter data, provides a robust framework for understanding rip current dynamics.</p> </ItemContent> <ItemContent> <p>Satellite-derived bathymetry offers a cost-effective method for mapping nearshore topography, improving rip current forecasting.</p> </ItemContent> <ItemContent> <p>AI-driven detection enables reliable global rip current identification, supporting real-time beach safety alerts.</p> </ItemContent> <ItemContent> <p>The traffic-light warning system at Rushikonda enhances lifeguard efficiency, promoting safer beaches and sustainable tourism.</p> </ItemContent> </UnorderedList></p>

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A multidimensional approach to manage rip current danger and enhancing safety at beaches

  • 期刊论文

  • 发表日期：

    2026年1月29日

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  • Surisetty V V Arun Kumar,
  • Madipally Ramesh,
  • Chintam Venkateswarlu,
  • B Gireesh,
  • T Sridevi,
  • L Sheela Nair,
  • Sanjib Kumar Deb,
  • C V Naidu,
  • Rashmi Sharma

摘要

Abstract

Rip currents pose a significant threat to beach safety, causing numerous drownings annually. This study integrates video monitoring (Video Beach Monitoring System – VBMS and Smartphone Beach Monitoring System – SBMS), satellite imagery, NavIC-based drifters, XBeach modelling, and YOLO-V5 Artificial Intelligence technology to enhance rip current detection and management at two rip-prone beaches: Rushikonda and RK Beaches, Visakhapatnam on east coast of India. We mapped 231 rip channels using high-resolution satellite data (2015–2017) and identified seasonal patterns via video analysis (2022–2023). Drifters validated rip velocities, while XBeach simulations (with RMSE 0.12 m/s) replicated dynamics. The AI model achieved >77% detection accuracy, reducing misses by combining video and satellite data. The Safe Beach portal delivers daily rip current forecasts for 175 Indian beaches using a statistical model, improving public safety. This approach, tested on Goa beaches (with ~70% AI accuracy), offers a scalable framework for global replication, enhancing tourism and reducing drowning risks; however, challenges remain in resource-limited regions.

Research highlights

Integrated video monitoring, satellite imagery, and AI technology effectively identified persistent rip channels at Rushikonda and RK Beaches, enhancing coastal hazard detection.

XBeach modelling, combined with NavIC drifter data, provides a robust framework for understanding rip current dynamics.

Satellite-derived bathymetry offers a cost-effective method for mapping nearshore topography, improving rip current forecasting.

AI-driven detection enables reliable global rip current identification, supporting real-time beach safety alerts.

The traffic-light warning system at Rushikonda enhances lifeguard efficiency, promoting safer beaches and sustainable tourism.