Urban Disaster Risk Assessment and Decision-making Model Based on Big Data and AI

Jinglin Wu ()
Additional contact information
Jinglin Wu: China University of Geosciences, School of Economics and Management

A chapter in Proceedings of the 2025 3rd International Conference on Digital Economy and Management Science (CDEMS 2025), 2025, pp 587-594 from Springer

Abstract: Abstract With the rapid development of urbanization, cities are facing various disaster risks. Traditional disaster risk assessment and decision-making methods have limitations in dealing with complex and dynamic urban environments. This paper focuses on the construction of an urban disaster risk assessment and decision-making model by integrating big data and artificial intelligence (AI) technologies. By collecting and analyzing a large amount of multi-source data related to urban disasters, such as geographical information, meteorological data, social and economic data, and historical disaster data, we can obtain a more comprehensive and accurate understanding of disaster risks. Advanced AI algorithms, including machine learning and deep learning, are employed to process and analyze these data to identify patterns, trends, and potential risk factors. The model not only provides accurate risk assessment results but also generates intelligent decision-making suggestions for disaster prevention, mitigation, and response. It can help urban managers and relevant departments make more scientific and timely decisions to reduce the losses caused by disasters. This research is of great significance for improving urban disaster resilience and ensuring the safety and sustainable development of cities.

Keywords: Big Data; AI; Urban Disasters; Risk Assessment; Decision-making Model (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-770-0_66

Ordering information: This item can be ordered from
http://www.springer.com/9789464637700

DOI: 10.2991/978-94-6463-770-0_66

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().