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AI-Driven Disaster Aid Prioritization System for Efficient Resource Allocation and Emergency Response

Sathishkumar K, Vasudevan S, Sampathkumar K, Sachin S, Dr. V. Kamalaveni

Abstract


Natural disasters such as floods, earthquakes, cyclones, and landslides often result in severe damage to human lives, infrastructure, and essential resources. One of the major challenges during such emergencies is ensuring that relief efforts are delivered quickly and efficiently, especially when resources are limited and conditions are constantly changing. Traditional disaster management approaches largely depend on manual assessment and human decision-making, which can lead to delays, inaccuracies, and unequal distribution of aid.

This project presents a Disaster Aid Prioritization System that utilizes machine learning techniques to enhance the decision-making process. The system analyzes key factors such as population size, level of damage, presence of medical emergencies, and availability of critical resources like water, shelter, and food. Based on these parameters, it classifies affected regions into priority levels such as High, Medium, and Low urgency.

By automating the prioritization process, the system reduces reliance on human judgment and improves response time. It provides a structured and data-driven approach for identifying the most critical areas, enabling authorities to allocate resources more effectively. The system is scalable, cost-efficient, and can be integrated with real-time data sources in the future, making it suitable for practical disaster response scenarios.


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References


V. Chamola, V. Hassija, S. Gupta, A. Goyal, M. Guizani, and B. Sikdar, “Disaster and pandemic management using machine learning: A survey,” IEEE Internet of Things Journal, vol. 8, no. 21, pp. 16047–16071, 2021.

V. Linardos, M. Drakaki, P. Tzionas, and Y. L. Karnavas, “Machine learning in disaster management: Recent developments in methods and applications,” Machine Learning and Knowledge Extraction, vol. 4, no. 2, pp. 446–473, 2022.

H. Jain, R. Dhupper, A. Shrivastava, and D. Kumar, “Leveraging machine learning algorithms for improved disaster preparedness and response,” Frontiers in Environmental Science, vol. 11, 2023.

R. Joshi, A. Shinde, S. Kelkar, and M. Deore, “Towards efficient disaster management: Role of machine learning, deep learning and WSN technologies,” International Journal of Intelligent Systems and Applications in Engineering, 2023.

X. Li and A. Mostafavi, “Machine learning approach for disaster risk and resilience assessment,” npj Natural Hazards, vol. 2, 2025.

M. Imran, C. Castillo, F. Diaz, and S. Vieweg, “Processing social media messages in mass emergency: A survey,” ACM Computing Surveys, vol. 47, no. 4, 2015.

Y. Yuan and M. Shaw, “Inductive learning for disaster management and emergency response,” Expert Systems with Applications, vol. 38, no. 5, pp. 5635–5643, 2011.

P. Sahu, S. Upadhyay, A. Singh, J. Sharma, and A. Nayak, “AI in disaster response: Real-time predictions and relief management,” International Journal of Novel Research and Development, 2024.

S. S. Rautaray and A. Agrawal, “Artificial neural network approaches for disaster management: A review,” International Journal of Disaster Risk Reduction, vol. 81, 2022.

K. Saldana Ochoa and T. Comes, “A machine learning approach for rapid disaster response based on multi-modal data,” arXiv preprint arXiv:2108.00887, 2021.


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