Modeling of Chemical Hazards Dispersion in Lakes and Reservoirs in CONUS

The objective of this study is to develop methods to adequately characterize the chemical and physical environments of lakes and reservoirs for the CONUS operational implementation within SHARC. Over the course of this project we will explore the novel and innovative integration of deep machine learning, data analytics, computational fluid mechanics and in-situ/overhead empirical data. Specifically, we will utilize self-organizing map techniques to classify physiochemical and geomorphological conditions within lakes and reservoirs, leverage the classification of the geomorphological conditions to develop characteristic wind-driven flow patterns within lakes and reservoirs, incorporate the derived information into SHARC’s existing database frameworks and leverage all the above information to develop the relevant questions and workflow to allow users of SHARC to conduct hazard assessment modeling within lakes and reservoirs.