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Smart city article details
| Title | Computer-Aided Resilience: Advanced Techniques For Disaster Management In Smart Urban Environments |
|---|---|
| ID_Doc | 15441 |
| Authors | Li R.; Di Y.; Tian H. |
| Year | 2024 |
| Published | Sustainable Cities and Society, 108 |
| DOI | http://dx.doi.org/10.1016/j.scs.2024.105437 |
| Abstract | This research paper explores innovative contributions to the field of disaster management in smart urban environments, with a particular focus on integrating advanced computer-aided techniques, specifically GRU-CNN. Three key contributions are highlighted: (1) the development of dynamic risk assessment algorithms utilizing GRU-CNN for real-time analysis and predictive modeling, enabling proactive disaster mitigation; (2) the establishment of an integrated sensor network infrastructure for early warning systems, leveraging various sensors and GRU-CNN-based data analytics to detect and respond to potential disasters at their nascent stages; and (3) the implementation of human-centric resilience planning, utilizing GRU-CNN-based computer-aided tools to simulate disaster scenarios and engage communities in preparedness efforts. The dynamic risk assessment algorithms presented in this paper, powered by GRU-CNN, enable continuous monitoring and analysis of diverse data sources, fostering a proactive approach to disaster preparedness. The integrated sensor network architecture enhances early warning capabilities, allowing for timely responses to emerging threats through the application of GRU-CNN methodologies. Furthermore, the human-centric resilience planning approach introduces virtual simulations using GRU-CNN to model disaster scenarios, facilitating the testing and refinement of strategies in a risk-free environment. This approach not only ensures the adaptability of plans but also engages and educates communities, fostering a culture of preparedness, with GRU-CNN playing a pivotal role in the process. Through these contributions, this research paper seeks to advance the discourse on disaster management in smart urban environments, emphasizing the integration and effectiveness of GRU-CNN in enhancing resilience and response strategies. By leveraging cutting-edge GRU-CNN technologies and prioritizing community involvement, the proposed techniques aim to provide a more robust and adaptive response to the challenges posed by natural and man-made disasters in urban settings. © 2024 Elsevier Ltd |
| Author Keywords | AI-driven disaster preparedness; Community engagement; Disaster resilience modeling; Smart city resilience; Urban risk management |
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