Real Time Disaster Information Aggregation Model

Abstract

Disastermanagementandresponserelyheavily on real-time and accurate information. Previous research has focused on collecting disaster data from official government reports and structured news sources. While these methods provide reliable information, they often suffer from delays and lack immediate public sentiment analysis. In our project, we enhance disaster response by extracting data from Reddit and Google News, allowing access to both real-time information and public sentiment. Using web scraping and Natural Language Processing (NLP) techniques, we filter relevant disaster-related postsandnewsarticles.Sentimentanalysisisperformedtoassess the emotional tone of public reactions. Additionally, by applying classification models, we categorize the severity of the events, providingauthoritieswithcrucialinsights.Thisintegratedsystem offers a more immediate, diverse, and sentiment-aware disaster information pipeline compared to traditional methods, aiming to improvethespeedandefficiencyofdisastermanagementefforts.

Introduction

The project focuses on developing a Real-Time Disaster Information Aggregation Software that collects, filters, and displays disaster-related data from multiple online sources such as news websites and Reddit. This system addresses the critical need for timely and accurate disaster information, which traditional reporting methods often fail to provide quickly enough during rapidly unfolding events like earthquakes, floods, and industrial accidents.

Using Python and Django for backend development, and HTML, CSS, JavaScript, and Bootstrap for the frontend, the platform offers a user-friendly dashboard with features like filtering by disaster type, pagination, and personalized alerts. It integrates data extraction tools (BeautifulSoup, PRAW) and employs Natural Language Processing and machine learning to categorize disaster information and assess severity in real-time.

The system enhances situational awareness for emergency responders and decision-makers by centralizing verified, up-to-date information, including interactive maps and live feeds. It overcomes challenges faced by existing disaster management solutions, such as slow updates, fragmented information, and lack of scalability.

The software’s architecture includes a PostgreSQL database for storing disaster data, with Flask APIs handling user requests and rendering a responsive web interface. The project aims to improve disaster response efficiency by enabling faster, broader, and more accurate access to critical information during emergencies.

Conclusion

The Disaster Information Dashboard successfully providesauser-friendlyplatformtoviewandfilterdisaster-relateddata efficiently. Using technologies like Flask, PostgreSQL, and HTML/CSS, the system demonstrates how data-driven web applications can assist in organizing and presenting critical information.Although the current system handles static data well, future enhancementssuchasreal-timedataintegrationandpredictive analysiscanmakeitevenmoreimpactful.Overall,thisproject offers a strong foundation for further development in the field of disaster management systems.

References

[1] Z.Bouzidi,M.Amad,andA.Boudries,“IntelligentandReal-TimeAlert Model for Disaster Management Based on Information Retrievalfrom Multiple Sources,” International Journal of Advanced Media andCommunication, ACM Digital Library, 2019. [2] M. Karimiziarani, “Social Media Analytics in Disaster Response: AComprehensive Review,” arXiv preprint, 2023. [3] M. F. I. Sumon, M. A. Khan, and A. Rahman, “Machine Learning forReal-Time Disaster Response and Recovery in the U.S.,” InternationalJournal of Machine Learning Research in Cybersecurity and ArtificialIntelligence, 2023. [4] O. Kotagiri, “Enhancing Disaster Response and Recovery Through AI:Real-Time Decision Support for Sustainable Humanitarian Efforts,” In-ternational Journal of AI-Assisted Medicine, 2022. [5] A. A. Shuaibu and S. Tiwari, “Real-Time Data Analysis for DisasterManagement: A Machine Learning Approach,” Journal of IntelligentDecision Technologies and Applications, 2024.

Copyright

Copyright © 2025 Rita Kadam, Sourabh Tagad, Shashin Dherange, Siddhi Kanade, Akash Mali. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.