ABSTRACT

This paper explores how crowdsourced social media data complements urban flood modelling to improve model performance and achieve a better classification of impacts. In addition to georeferencing flood impacts, Twitter allows monitoring the events in terms of hazards and impacts, and YouTube facilitates a retrospective analysis from audiovisual data. The analysis of 2800 tweets collected during four storm events and of almost 900 videos of the recent history of the basin, together with the implementation of a high-resolution model, contributed to the expansion of the capacity to represent the temporal and spatial scales of the problem. The complementation of crowdsourced social media data and urban modelling enhances the understanding of the flood dynamics, thus offering a framework of greater certainty for the generation of flood risk management products.

摘要

本文探讨了众包社交媒体数据如何补充城市洪水建模以提高模型性能并实现更好的影响分类。除了地理参考洪水影响之外，Twitter 还允许根据危害和影响来监控事件，而 YouTube 有助于对视听数据进行回顾性分析。对四次风暴事件期间收集的 2800 条推文和近 900 个流域近期历史视频的分析，以及高分辨率模型的实施，有助于扩大表示该流域时间和空间尺度的能力。问题。众包社交媒体数据和城市建模的互补增强了对洪水动态的理解，