A global database of historic and real-time flood events based on social media
A global database of historic and real-time flood events based on social media
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DOI：
10.1038/s41597-019-0326-9
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发表时间：
2019-12-09
期刊：
影响因子：
9.8
通讯作者：
Aerts, Jeroen C. J. H.
中科院分区：
文献类型：
--
作者：
de Bruijn, Jens A.;de Moel, Hans;Aerts, Jeroen C. J. H.
及早发现事件并作出反应，可显著减少洪灾的社会影响。目前，预警系统依赖于量规、雷达数据、模型和非正式的当地来源。然而，这些系统的范围和可靠性是有限的。最近，使用社交媒体来检测灾害已经显示出令人振奋的结果，特别是在地震方面。在这里，我们提出了一个新的数据库，用于使用Twitter在全球范围内实时检测洪水。该方法是使用8800万条推文开发的，从这些推文中，我们在短短四年多的时间里以11种语言得出了176个国家的10,000多起洪水事件(即，发生在一个国家或一级行政区划的洪水)。使用严格的参数，验证表明大约90%的事件被正确检测到。在包含第一种官方语言的国家/地区中，我们的算法检测到了NatCatService灾难数据库中管理1级的63%的事件。此外，还检测到了大量未包括在NatCatService中的洪水事件。
Early event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real-time on a global scale using Twitter. The method was developed using 88 million tweets, from which we derived over 10,000 flood events (i.e., flooding occurring in a country or first order administrative subdivision) across 176 countries in 11 languages in just over four years. Using strict parameters, validation shows that approximately 90% of the events were correctly detected. In countries where the first official language is included, our algorithm detected 63% of events in NatCatSERVICE disaster database at admin 1 level. Moreover, a large number of flood events not included in NatCatSERVICE were detected.