AI-Powered Disaster Relief Resource Optimizer

Abstract

This innovative AI-Driven Disaster Relief Resource Optimization System is designed to enhance disaster response and improve the efficiency of resource distribution during earthquake emergencies. Its main goal is to utilize real-time data and sophisticated machine learning algorithms to facilitate the fair and effective allocation of aid to impacted areas. This initiative tackles prevalent issues in disaster management, such as delays, inefficiencies, and the constraints of conventional manual approaches. By utilizing real-time demographic data, damage assessment indicators, and resource availability, the system effectively prioritizes urgent needs and reduces response times. With ongoing monitoring and feedback, the system is intended to adapt and respond to the evolving requirements of earthquake disaster management, establishing a strong foundation for efficient and sustainable disaster relief efforts.

Introduction

The growing frequency and severity of earthquakes worldwide demand smarter, faster, and more adaptable disaster relief systems. Traditional disaster response methods rely heavily on manual processes and rigid protocols, leading to delays and inefficient resource distribution that worsen the impact on affected communities. To overcome these challenges, a web-based AI system has been developed that integrates real-time earthquake data, machine learning, and predictive algorithms to optimize disaster relief efforts.

This AI system evaluates factors such as earthquake magnitude, population density, and resource availability to prioritize aid delivery to the most affected areas quickly and fairly. Its dynamic, adaptive nature allows continuous learning and adjustment as new data arrives, improving decision-making, reducing response times, and minimizing resource waste.

The literature review highlights advancements in disaster management, emphasizing IoT, remote sensing, and machine learning for better prediction and response. A hybrid deep learning model combining Residual Networks, Gated Recurrent Units, and optimization techniques shows promise in accurately detecting floods and earthquakes.

Methodologically, the system gathers earthquake data from the USGS, calculates population density using geospatial data, and assigns priorities for resource allocation based on severity and population impact. It optimizes resource distribution by grouping nearby high-priority areas and continually updates priorities as new data emerges.

Technologies employed include Python, Flask, JavaScript, Folium for mapping, and SQLite for data storage, integrated via APIs for real-time data access. Machine learning models such as Random Forest Classifiers predict disaster severity, while population regression models estimate affected populations to guide resource distribution.

Results demonstrate the system’s effectiveness in accurately predicting disaster severity, optimizing resource allocation, and supporting timely, informed decision-making during earthquake emergencies.

Conclusion

The Disaster Relief Resource Optimizer is designed to provide real-time responses to emergencies like earthquakes. It prioritizes critical areas based on earthquake alerts and resource availability, ensuring that regions with the highest need receive support first. The system streamlines resource allocation, updates databases efficiently, and enables new user registration for better coordination. A user-friendly dashboard allows for easy navigation of alerts, resources, and updates, ensuring transparency and informed decision-making. This system enhances disaster preparedness, minimizes the impact of emergencies, and ensures timely and effective relief efforts.

References

[1] S. M. Khan, I. Shafi, W. H. Butt, I. de la Torre Diez, M. A. López Flores, J. C. Galán, and I. Ashraf, “A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions,” Land, vol. 12, no. 8, 2023. doi: 10.3390/land12081514. [2] P. K. E, V. N. Thatha, G. Mamidisetti, S. V. Mantena, P. Chintamaneni, and R. Vatambeti, “Hybrid Deep Learning Model with Enhanced Sunflower Optimization for Flood and Earthquake Detection,” Heliyon, vol. 9, no. 10, 2023, article e21172. doi: 10.1016/j.heliyon.2023.e21172. [3] R. Damaševi?ius, N. Bacanin, and S. Misra, “From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management,” J. Sens. Actuator Netw., vol. 12, no. 3, pp. 41, 2023. doi: 10.3390/jsan12030041. [4] Ian Sommerville, Software Engineering, Pearson Education ISBN 13: 978-0-13-703515-19th Edition 2011

Copyright

Copyright © 2025 P Pavithra, N Neethi, Rasineni Banu Prasad, Ronith R, Noorullah Shariff C. 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.