Flood Analytics Information System (FAIS)

Flood Analytics Information System (FAIS) is a data analytics application funded by the National Science Foundation to integrate crowd intelligence, machine learning, and natural language processing of tweets for flood situational awareness. This national scale prototype combines flood peak rates and river level information with geotagged tweets to identify a dynamic set of at-risk locations to flooding. FAIS can help users to (i) collect georeferenced tweets, traffic and the USGS webcam images in real time to identify at-risk locations, (ii) detect label and score the objects in time-lapse flooded and non-flooded images, and (iii) perform flood frequency analysis (FFA) using various probability distributions with the associated uncertainty estimation to assist engineers in designing safe structures. FAIS is successfully tested in real-time during several hurricane driven flooding events across the south and southeast US where the storms made extensive disruption to critical infrastructure and communities. FAIS is freely accessible to everyone, but primarily designed for real-time and operational use. FAIS video tutorial and educational material are freely available to researchers, students, and professionals via Hydrosystem and Hydroinformatics Research (HHR) group at Clemson University.

洪水分析信息系统（Flood Analytics Information System，简称FAIS）系由美国国家科学基金会资助的数据分析应用，旨在整合群体智能、机器学习及对推文的自然语言处理，以提升对洪水形势的感知能力。该国家级原型系统将洪水峰值流速与河流水位信息与地理标记的推文相结合，以识别受洪水威胁的动态位置。FAIS能够帮助用户：（一）实时收集地理参照推文、交通数据以及美国地质调查局（USGS）的网络摄像头图像，以识别受威胁区域；（二）对洪水与非洪水的时间序列图像中的对象进行检测、标记和评分；（三）利用各种概率分布进行洪水频率分析（Flood Frequency Analysis，简称FFA），并估算相关不确定性，以协助工程师设计安全结构。FAIS在多次由飓风引发的美国南部和东南部洪水事件中成功进行了实时测试，这些事件对关键基础设施和社区造成了广泛的破坏。FAIS对所有用户免费开放，但主要设计用于实时和操作使用。FAIS的视频教程和教育资料可通过克莱姆森大学的Hydrosystem和Hydroinformatics Research（HHR）小组免费提供给研究人员、学生和专业人士。