With the rapid development of the Internet of Things (IoT) and Big Data infrastructure, crowdsourcing techniques have emerged to facilitate data processing and problem solving particularly for flood emergences purposes. A Flood Analytics Information System (FAIS) has been developed as a Python Web application to gather Big Data from multiple servers and analyze flooding impacts during historical and real-time events. The application is smartly designed to integrate crowd intelligence, machine learning (ML), and natural language processing of tweets to provide flood warning with the aim to improve situational awareness for flood risk management. FAIS, a 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. The prototype was successfully tested in real-time during Hurricane Dorian flooding as well as for historical event (Hurricanes Florence) across the Carolinas, USA where the storm made extensive disruption to infrastructure and communities.

随着物联网（IoT）和大数据基础设施的快速发展，出现了众包技术，以促进数据处理和问题解决，尤其是针对洪水涌现的目的。洪水分析信息系统（FAIS）已开发为Python Web应用程序，可从多个服务器收集大数据并分析历史和实时事件期间的洪水影响。该应用程序经过精心设计，集成了人群情报，机器学习（ML）和推文的自然语言处理功能，可提供洪水预警，旨在提高对洪水风险管理的态势感知。FAIS是一种国家规模的原型，将洪水峰值速率和河流水位信息与带有地理标记的推文相结合，以识别出一系列充满洪水风险的动态位置。