Decision Briefing Generation on Meteorological Social Events

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Meteorological disasters have the characteristics of suddenness, complexity, dynamics, and diversity, which have inflicted severe challenges on emergency decision support services in disaster environments. Social sensors collect data from mobile devices worn or carried by humans, intuitively perceive the environmental conditions associated with a meteorological disaster through human senses, and have the advantages of comprehensive coverage, communication in real-time, and low cost. Decision briefing is an effective support mode of emergency management services for sudden meteorological disasters, and it is also the primary way to deliver meteorological decision knowledge. This thesis focuses on the critical technologies for the automatic generation of meteorological event knowledge-enhanced decision briefing based on social sensor signals, proposing the co-occurrence feature-based sudden meteorological event detect model, multiple knowledge-enhanced meteorological decision briefing generation model, and meteorological event knowledge-enhanced decision briefing optimization model. In addition, this thesis reports a prototype application of the meteorological decision briefing integrated with the above research foci, which showcases the use of social sensor signals to provide feedback on daily meteorological events and then provides decision support services based on meteorological decision briefing.
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