Analysis of Emergency Management in Smart Cities Based on Artificial Intelligence Technology
With the acceleration of urbanization, cities are facing increasing risks of emergencies and disasters. How to effectively enhance the emergency response capabilities of cities has become an important issue in modern urban management. This study focuses on building a smart city emergency system based on artificial intelligence technology, aiming to integrate advanced AI algorithms and big data analysis methods to achieve rapid identification, accurate prediction, and efficient response to urban emergencies. This article first outlines the challenges faced by emergency management systems in the current construction of smart cities, including data silos, response lag, and other issues. Subsequently, the potential of AI technology in improving the accuracy of emergency warning, optimizing resource allocation, and enhancing decision support was discussed in detail. Through the analysis of practical cases, it demonstrates how AI driven urban emergency systems can play a critical role in natural disasters such as fires, floods, and public health emergencies. Finally, the issues that the system needs to face in terms of privacy protection, ethical considerations, and technology popularization were discussed, and prospects for future development directions were proposed. Research has shown that AI based smart city emergency systems can not only significantly improve the safety and resilience of cities, but also provide a new path for promoting the intelligent transformation of urban management.
Authors
- Xiaodong Tang
- Y Zhang
- Gaofeng Xu (ORCID: https://orcid.org/0009-0009-0574-8177)
- Dong Wang (ORCID: https://orcid.org/0009-0000-5790-8026)
- Yurong Wang
- Kai Sun (ORCID: https://orcid.org/0000-0002-5319-6559)
Institutions
- Hubei Urban Construction Vocational and Technological College (CN)
Publication Details
- Published
- 2025-03-14
- DOI
- https://doi.org/10.1145/3746709.3746914
- Primary Topic
- Safety and Risk Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00