DISSERTATION DEFENSE Department of Computer Science and Engineering University of South Carolina Author : Nattapon Donratanapat Advisor : Dr. Jose Vidal and Dr. Vidya Samadi Date : Nov 7th , 2019 Time : 2:00 pm Place : Meeting Room 2267, Innovation Center Abstract With the rapid development of the Internet and mobile devices, crowdsourcing techniques have emerged to facilitate data processing and problem solving particularly for flood emergences purposes. We developed Flood Analytics Information System (FAIS) application as a python interface to gather Big data from multiple servers and analyze flood hazards in real time. The interface uses crowd intelligence and machine learning to provide flood warning and river level information, and natural language processing of tweets during flooding events, with the aim to improve situational awareness for flood risk managers and other stakeholders. We demonstrated and tested FAIS across Lower PeeDee Basin in the Carolinas where Hurricane Florence made extensive damages and disruption. Our research aim was to develop and test an integrated solution based on real time Big data for stakeholder map-based dashboard visualizations that can be applicable to other countries and a range of weather-driven emergency situations. The application allows the user to submit search request from USGS and Twitter through criteria, which is used to modify request URL sent to data sources. The prototype successfully identifies a dynamic set of at-risk areas using web-based river level and flood warning API sources. The list of prioritized areas can be updated every 15 minutes, as the environmental information and condition change.