Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)

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Real Time Artificial Intelligence Powered Engagement Platforms for Proactive Disaster Management Emergency Response and Risk Mitigation

Authors
Purushotham Endla1, *, Sharmila Bandlamudi2, N. Sasirekha3, S. Saroja Devi4, Sumit Pokhriyal5, 6, P. Nandhini7
1Department of Physics, School of Sciences and Humanities, SR University, Warangal, Telangana, India
2Assistant Professor, Department of CSE, CVR College of Engineering, Hyderabad, Telangana, India
3Associate Professor, Department of ECE, Sona College of Technology, Salem, Tamil Nadu, India
4Assistant Professor, Department of Information Technology, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India
5Assistant Professor, Department of Physics, Graphic Era Hill University, Dehradun, India
6Adjunct Professor, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
7Assistant Professor, Department of Mathematics, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India
*Corresponding author. Email: psm45456@gmail.com
Corresponding Author
Purushotham Endla
Available Online 23 May 2025.
DOI
10.2991/978-94-6463-718-2_79How to use a DOI?
Keywords
Artificial Intelligence; disaster management; emergency response; risk mitigation; real-time analysis; machine learning; deep learning; data analytics; community resilience
Abstract

Developing effective disaster management and risk reduction strategies is essential, and the proliferation of natural and man-made disasters increasingly calls for proactive and innovative solutions that enhance capacity for disaster management and risk mitigation. The study aims to develop AI-powered real-time engagement platforms that facilitate better decision-making, resource optimization, and community resilience in emergencies. The system presents a machine-learning, deep learning and data analytics-based platform that facilitates real-time disaster scenarios to offer the ground opportunity fault within the environment. AI is essential in processing disaster event data, forecasting disasters, and supporting fast and accurate decision-making, according to the study. Moreover, the platform is designed to be scalable, adaptable, and integrable into current emergency management systems, allowing it to be functional in varied disaster contexts. The AI system proposed could be an outburst for risk mitigation, as it would enable better coordination among disaster response teams and further ensure better readiness for any future crises. In practical terms, this paper advances the domain of disaster management by using the latest AI technology to promote the development of stronger, more effective, and preventive solutions to emergency response.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
Series
Advances in Computer Science Research
Publication Date
23 May 2025
ISBN
978-94-6463-718-2
ISSN
2352-538X
DOI
10.2991/978-94-6463-718-2_79How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Purushotham Endla
AU  - Sharmila Bandlamudi
AU  - N. Sasirekha
AU  - S. Saroja Devi
AU  - Sumit Pokhriyal
AU  - P. Nandhini
PY  - 2025
DA  - 2025/05/23
TI  - Real Time Artificial Intelligence Powered Engagement Platforms for Proactive Disaster Management Emergency Response and Risk Mitigation
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 932
EP  - 943
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_79
DO  - 10.2991/978-94-6463-718-2_79
ID  - Endla2025
ER  -