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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63601

Result on Disaster-Aware Blood Donation Network using Geo-Alerts and Predictive Shortage Modeling

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63601

Volume:

10 |

5 |

May 2026

Pages:

1-3

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KP

Kadambari Pardeshi

Computer

Matoshri College Of Engineering And Research Centre, Nashik

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SS

Sakshi Sanap

Computer

Matoshri College Of Engineering And Research Centre, Nashik

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NM

Neha More

Computer

Matoshri College Of Engineering And Research Centre, Nashik

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SU

Sakshi Ugale

Computer

Matoshri College Of Engineering And Research Centre, Nashik

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Abstract

Traditional blood donation networks often lack the real-time communication and predictive planning necessary to respond effectively during disasters. This project proposes a framework that integrates geo-location alerts with machine learning-driven predictive modeling to improve emergency response. By analysing historical donation and usage trends, the system forecasts potential shortages to allow for proactive inventory management. Real-time geo-tracking and smart routing minimize delays and ensure the availability of blood in crisis scenarios.

KEYWORDS

Keywords— Blood Donation Network Geo-Alerts Disaster Response Predictive Shortage Modeling Healthcare Informatics

Cite This Article

Kadambari Pardeshi, Sakshi Sanap, Neha More, Sakshi Ugale, "Result on Disaster-Aware Blood Donation Network using Geo-Alerts and Predictive Shortage Modeling," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-3, May 2026.

Full Article/Conclusion

The Disaster-Aware Blood Donation Network provides an intelligent solution for emergency blood management by centralising communication and using predictive analytics. The framework minimizes delivery delays and enhances healthcare resilience during crises. Future enhancements may include IoT-enabled monitoring, cloud scalability, and blockchain security to further protect data and improve response management.

References

[1]           M. Patel and K. Rao, “Disaster-Aware Blood Supply Chain Optimization using IoT and Machine Learning,” 2024. [2]           P. Roy and S. Banik, “Real-Time Blood Availability Prediction using Machine Learning Algorithms,” 2023.   [3]           D. Williams and M. Cooper, “Emergency Healthcare Communication System using Mobile Notification Services,” 2024.   [4]           World Health Organization (WHO), “Blood Safety and Availability Report,” 2024. [5]           S. Lee, H. Kim, and Y. Park, “Artificial Intelligence in Healthcare Emergency Management: A Survey,” 2023. [6]           H. Chen and X. Liu, “Deep Learning Approaches for Predicting Blood Demand and Inventory Management,” 2024.   [7]           Deshmukh and P. Kulkarni, “Geo-Location Based Emergency Blood Donor Tracking System,” 2023. [8]           V. Sharma and A. Gupta, “Disaster Management using AI, IoT, and Cloud Computing Technologies,” 2023.

Editorial & Review Transparency

Publication Date

5/21/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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