Model-data matching method for natural disaster emergency service scenarios: implementation based on a knowledge graph and community discovery algorithm

Liu, Honghao; Hu, ZhuoWei; Yang, Ziqing; Wang, Mi

doi:10.1007/s11069-023-06360-9

Cited by 1 publication

(1 citation statement)

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“…Node importance analysis and community discovery algorithms are used to analyze the community structure of essential nodes, and experiments validate the typical rainstorm and flood disaster risk assessment emergency service scenario. The results show that the constructed knowledge map of natural disaster emergency services can support the formal expression of semantic associations among disaster scenarios, model methods, and disaster data [6]. Zou et al analyzed the nature of urban rainstorm disaster events, analyzed their components and dynamic characteristics from the occurrence mechanism of urban rainstorm disaster events, and proposed a multi-level knowledge representation model consisting of event layer, object state layer, feature layer, and relationship layer.…”

Section: Multimodal Knowledge Graphmentioning

confidence: 99%

Knowledge Management Model for Urban Flood Emergency Response Based on Multimodal Knowledge Graphs

Li,

Yuan,

et al. 2024

Water

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Recently, frequent flood disasters in China have seriously threatened economic development and public safety. This paper addresses the need for a dynamic urban flood emergency knowledge management system in emergency management departments and the lack of systematic knowledge among emergency managers regarding urban flood control. A multimodal knowledge graph-based urban flood emergency knowledge management model was constructed to enhance the decision-making capabilities of emergency management departments, improve the efficiency of public emergency evacuation, and reduce losses from urban flood disasters by analyzing the shortcomings of the existing emergency management system. An intelligent and dynamic flood emergency knowledge management model was built. This paper integrates multimodal knowledge graph technology to establish a multimodal emergency knowledge management framework for urban flood control. It develops and simulates the proposed model’s application scenarios for urban flood emergency evacuation using the Flocking algorithm on the NetLogo platform. Through simulation experiments, the practicality and effectiveness of the model in real flood disaster situations were examined, particularly in simulating crowd evacuation behavior. The study found that the model significantly improves the accuracy of information and decision-making speed during emergency responses and supports emergency management departments in conducting targeted and personalized emergency decisions. This research provides a scientific basis for emergency management departments to optimize their emergency response strategies to flood disasters and serves as a reference and example for the application of multimodal knowledge graph technology in emergency management.

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Section: Multimodal Knowledge Graphmentioning

confidence: 99%