AI Agency in Information Retrieval: Advancing Data Access Through Natural Language Interfaces; Enhancing Global Accessibility and Information Flow: Bridging Language Divides with Natural Language Processing and Deep Learning

This research explores the integration of Natural Language Processing (NLP) into disaster management systems, with a focus on improving flood prediction and information dissemination. the NLP feature within the project 'Floodwatch,' aims to make advanced flood prediction technologies accessible to all citizens. By incorporating NLP into the Floodwatch web application platform, we seek to bridge the gap between complex flood data and the average user, ensuring inclusion of all citizens.

The project is grounded in the Social Construction of Technology (SCOT) theory, which mentions that technological development is influenced by social factors. Our approach leverages NLP to make flood data more digestible, presenting it in an easily understandable format. This effort aligns with the SCOT framework by acknowledging the diverse information needs of various social groups.

This research demonstrates the potential of NLP to enhance disaster management practices by making critical information more accessible for the public. This project emphasizes the need of user-centric design in technological solutions to ensure software is accessible to all regardless of physical limitations or language barriers. The findings aim to contribute to a broader understanding of how technology can be deployed to empower communities, particularly in the context of disaster preparedness and response on a global scale.