Floods are the most common and among the most severe natural disasters in many countries around the world. As global warming continues to exacerbate sea level rise and extreme weather, governmental authorities and environmental agencies are facing the pressing need of timely and accurate evaluations and predictions of flood risks. Current flood forecasts are generally based on historical measurements of environmental variables at monitoring stations. In recent years, in addition to traditional data sources, large amounts of information related to floods have been made available via social media. Members of the public are constantly and promptly posting information and updates on local environmental phenomena on social media platforms. Despite the growing interest of scholars towards the usage of online data during natural disasters, the majority of studies focus exclusively on social media as a stand-alone data source, while its joint use with other type of information is still unexplored. In this paper we propose to fill this gap by integrating traditional historical information on floods with data extracted by Twitter and Google Trends. Our methodology is based on vine copulas, that allow us to capture the dependence structure among the marginals, which are modelled via appropriate time series methods, in a very flexible way. We apply our methodology to data related to three different coastal locations in the South cost of the UK. The results show that our approach, based on the integration of social media data, outperforms traditional methods, providing a more accurate evaluation and prediction of flood events.
翻译:由于全球变暖继续加剧海平面上升和极端天气,各国政府当局和环境机构正面临迫切需要及时准确评估和预测洪水风险的迫切需求,目前洪水预测一般以监测站环境变量的历史测量为基础。近年来,除了传统数据来源外,还有大量与洪水有关的信息通过社交媒体提供。公众成员不断和迅速地在社交媒体平台上发布当地环境现象的信息和更新信息。尽管学者们越来越关注在自然灾害期间使用在线数据,但大多数研究的重点完全放在社交媒体上,将其作为独立的数据来源,而与其他类型信息的共同使用仍然没有被探索。本文建议,通过将传统洪水历史信息与Twitter和谷歌趋势所提取的数据相结合来填补这一空白。我们的方法以葡萄干云为基础,使我们能够以非常灵活的方式通过适当时间序列方法模拟的边缘地区依赖性结构。我们采用我们的方法,将社会媒体的数据作为独立的独立数据,同时在三个不同的沿海地点共同使用。我们用传统的方法,即以南方成本来显示我们基于社会媒体的整合。