A Study on Disaster Management System in Government Women’s Polytechnic Patna by Using Sensors

Abstract

A disaster management system (DMS) is an organized framework—often supported by technology—that coordinates prevention, preparedness, response, and recovery actions before, during, and after a disaster. It aims to minimize loss of life, reduce damage to property and infrastructure, and restore normalcy as quickly as possible. Key idea of the Abstract: Typically, an abstract of a disaster management system describes a platform that integrates early?warning mechanisms, real?time data monitoring, and communication tools to support rapid decision?making by authorities and communities. It emphasizes phases such as risk assessment, preparedness exercises, coordinated emergency response, and post?disaster recovery, often using modern tools like GIS, AI, and cloud services. Common features highlighted: • Predictive analytics and early warning alerts for events such as earthquakes, floods, or cyclones. • Centralized resource and incident management, including tracking of relief materials, shelters, and rescue teams. • Multi?agency coordination and role?based access to ensure efficient and secure communication during emergencies.

Introduction

The text explains a disaster management system as a structured approach to reduce the impact of natural and human-made disasters through four main stages: mitigation, preparedness, response, and recovery. Its main aim is to protect lives, property, and the environment while improving community resilience.

The need for disaster management is highlighted by the increasing frequency and severity of disasters caused by climate change, urbanization, and population growth. It helps save lives, reduce economic losses, protect the environment, ensure continuity of essential services, and support vulnerable populations. The scope includes studying the full disaster cycle along with policies, case studies, and practical training such as simulations and certifications.

The proposed methodology describes a simple, cost-effective sensor-based disaster detection system that can identify earthquakes, fire, and railway track cracks. It uses sensors like vibration, smoke, and crack detectors connected to a circuit that triggers a buzzer alarm when abnormal conditions are detected. The system was developed through planning, data collection, circuit design, and testing, with future improvements suggested such as advanced sensors and automated emergency responses.

In conclusion, effective disaster management is not only about responding to emergencies but also about prevention, awareness, and preparedness. A well-designed system improves safety, reduces damage, and speeds up recovery, especially when supported by coordinated efforts between government, organizations, and communities, along with modern early-warning technologies.

Conclusion

In conclusion, an effective disaster?management system is not just about reacting to disasters but about building a culture of safety, awareness, and readiness at all levels of society. When properly planned, implemented, and regularly updated, such a system can significantly reduce human suffering, protect infrastructure, and speed up recovery, making communities more capable of facing future hazards. A good disaster?management system aims to prevent and reduce risks (mitigation), prepare institutions and people (preparedness), act quickly and efficiently during a crisis (response), and then restore normal life and rebuild better (recovery). It also emphasizes strong coordination among government agencies, NGOs, local bodies, and the public, along with use of modern tools like early?warning systems and GIS?based risk mapping.

References

[1] Khan, Saad Mazhar, Imran Shafi, Wasi Haider Butt, Isabel de la Torre Diez, Miguel Angel López Flores, Juan Castanedo Galán, and Imran Ashraf. “A systematic review of disaster management Systems: approaches, challenges, and future directions.” Land 12, no. 8 (2023): 1514. [2] Ridzwan, Nurafiqah Syahirah Md, and Siti Harwani Md Yusoff. “Machine learning for earthquake Prediction: a review (2017–2021).” Earth Science Informatics 16, no. 2 (2023): 1133-1149. [3] Shah, S. A., Seker, D. Z., Hameed, S., & Draheim, D (2019). The Rising Role of massive Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects. IEEE Access, 1–1. Doi:10.1109 access.2019.2913340. [4] Jamshed, M. A., Ayaz, F., Kaushik, A., Fischione, C., & UrRehman, M. (2023). Green UAV-enabled Internet-of-Things Network with AI-assisted NOMA for Disaster Management. arXiv preprint arXiv:2304.13802.

Copyright

Copyright © 2026 Indrani ., Suhani Kumari, Nandani Kumar, Priyanshu Kumari, Putul Kumari, Reshmi Kumari, Nikita Kumari, Saubhagya Sambhav. 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.