Rescue Connect: AI-Powered Disaster Relief System

Abstract

An effective reaction in the case of disaster based on the rapid popularity of warnings, voluntary coordination, effective resource management and real -time communication. This article presents Rescue Connect, a Python -based disaster management system integrated chatbot\'s answers provided by AI, visualized resources and alerting processing. The system supports citizens\' interaction through common questions and security advice, visualization of volunteer status and resources and simulating new emergency warnings. By taking advantage of local logic and external API, the system provides a hybrid intelligent assistant to improve the preparation for the disaster and the reaction\'s coordination

Introduction

Disasters, both natural (floods, earthquakes) and artificial (industrial accidents), increasingly impact communities due to factors like climate change and urbanization. Effective, timely disaster response systems are essential but traditional systems face challenges such as delayed data integration, poor public engagement, fragmented communication, and limited adaptability.

To address these issues, the paper proposes RescueConnect, a lightweight Python-based disaster management system that integrates real-time alerts, volunteer coordination, resource monitoring, and an AI chatbot for emergency support. Designed for easy deployment at local or regional levels, it uses automation, data visualization (via Matplotlib), and rule-based AI to simulate and manage disaster scenarios in an interactive, command-line environment (Google Colab).

Key features include:

• Dynamic real-time disaster warnings generated randomly to simulate emergencies.

• Volunteer management with status tracking and location data, visualized in pie charts for quick decision-making.

• Resource monitoring for essentials like food, water, and medical kits, presented via bar and pie charts.

• An AI chatbot that provides basic emergency advice based on predefined rules.

• Modular design allowing independent testing and easy expansion.

RescueConnect improves disaster preparedness by providing a practical, accessible tool combining data, simulation, and user interaction for training, research, or real-time use, overcoming limitations of traditional systems by fostering better coordination and faster responses.

Conclusion

The Rescue Connect system is an impressive illustration of the potential that digital tools possess to greatly enhance the coordination and delivery of disaster relief efforts, when well designed. As a simulation tool developed, the system integrates real-time tracking of data, volunteer organization, resource management, and AI-driven communication seamlessly into one single platform. Its modular design makes it possible for users to interact with individual elements in isolation, yet reap the benefits of an interdependent system facilitating integrated disaster response planning and operation. By sending live alerts of disaster incidents, rich analysis of volunteer availability, and real-time resource stock levels, Rescue Connect facilitates better and more timely decisions that are crucial in pressure-packed emergency situations. Incorporation of a chatbot interface also provides an added value to user interaction in terms of easy support and information sharing, especially for the individuals looking for shelter, emergency numbers, or information on volunteering. Pie charts and bar graphs are used to enhance situational awareness and facilitate easy access by both technical and non-technical users. The capability of the system to replicate real disaster situations with dynamic alerts also makes it a useful training resource for emergency readiness. Overall, Rescue Connect demonstrates how a properly designed, Python program can bring together communication, analytics, and resource tracking to facilitate strategic, scalable, and user-friendly disaster management solutions.

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Copyright

Copyright © 2025 Manda Rushitha, Kakani Nikesh , Kudithi Sai Nitheesh, Dr. S Neduncheliyan. 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.