Combination of Large Language Models and Portable Flood Sensors for Community Flood Response: A Preliminary Study

The effectiveness of early warning systems can help people take action to mitigate the impact of extreme weather events once warnings are issued. The early warning systems developed by public agencies usually issue standard messages that, in many situations, may not affect all the people who receive the messages. In the long run, this can lead to behaviors in people who may not respond to relevant warnings, resulting in inefficiency. Users demand faster and more customized information that matches their needs, such as “How does this affect me right now?” or “What can I do to mitigate the impact?” This study proposes a decentralized framework at the community level that includes custom Internet of Things (IoT) sensors for timely information monitoring and large language models (LLMs) for the generation of user-defined warning messages. The sensors have the advantages of easy installation, low cost, and affordable maintenance fees. The trained LLMs expedite information processing given specific prompts and generate customized response messages to the users. In addition, the framework is established within a serverless environment, enabling rapid deployment and scalability. This integration of IoT sensors and LLMs demonstrates how the system performs once sensors detect flooding and how LLMs can deliver real-time, efficient, and localized action-ready information in different scenarios. This combination significantly enhances the responsiveness during flood events.