17:15 〜 18:45
[HDS09-P11] Research on Disaster Education Using AI Image Recognition for Rip Currents
キーワード：Rip Currents, Artificial Intelligence, Image Recognition, Disaster Education
Rip currents are powerful and hidden ocean hazards that cause hundreds of drowning deaths each year. This research aims to develop a system that utilizes artificial intelligence for real-time image recognition to identify rip currents and integrate disaster education content to raise public awareness and response capabilities to rip currents.
The research team will collect video materials containing rip currents, combined with ocean data for data preprocessing. Deep learning techniques such as CNN will be used to develop an AI model for rip current image recognition and classification, continuously optimizing the model's performance. Understandable educational content will be designed to convey knowledge about rip currents and response strategies. Finally, the AI model and educational content will be integrated.
The research results are expected to effectively improve public recognition and disaster prevention awareness of rip currents, reducing disaster losses. It will also promote the improvement of government disaster response policies, provide data for meteorology and oceanography research, and foster international cooperation in addressing transnational natural disasters. This research will make a significant contribution to enhancing local disaster prevention capabilities.
The research team will collect video materials containing rip currents, combined with ocean data for data preprocessing. Deep learning techniques such as CNN will be used to develop an AI model for rip current image recognition and classification, continuously optimizing the model's performance. Understandable educational content will be designed to convey knowledge about rip currents and response strategies. Finally, the AI model and educational content will be integrated.
The research results are expected to effectively improve public recognition and disaster prevention awareness of rip currents, reducing disaster losses. It will also promote the improvement of government disaster response policies, provide data for meteorology and oceanography research, and foster international cooperation in addressing transnational natural disasters. This research will make a significant contribution to enhancing local disaster prevention capabilities.