  This project was the first attempt to use big data analytics approaches and
machine learning along with Natural Language Processing (NLP) of tweets for
flood risk assessment and decision making. Multiple Python packages were
developed and integrated within the Flood Analytics Information System (FAIS).
FAIS workflow includes the use of IoTs-APIs and various machine learning
approaches for transmitting, processing, and loading big data through which the
application gathers information from various data servers and replicates it to
a data warehouse (IBM database service). Users are allowed to directly stream
and download flood related images/videos from the US Geological Survey (USGS)
and Department of Transportation (DOT) and save the data on a local storage.
The outcome of the river measurement, imagery, and tabular data is displayed on
a web based remote dashboard and the information can be plotted in real-time.
FAIS proved to be a robust and user-friendly tool for flood data analysis at
regional scale that could help stakeholders for rapid assessment of flood
situation and damages. FAIS also provides flood frequency analysis (FFA) to
estimate flood quantiles including the associated uncertainties that combine
the elements of observational analysis, stochastic probability distribution and
design return periods. FAIS is publicly available and deployed on the
Clemson-IBM cloud service.
