Computational methods and autonomous robotics systems for modeling and predicting harmful cyanobacterial blooms

Abstract This extended abstract describes a joint effort to model and predict harmful cyanobacterial blooms in lakes of an interdisciplinary team with expertise in big data, environmental science, ecology, human demography, instrumentation, and robotics from four states: Maine, New Hampshire, Rhode Island, and South Carolina. This project uniquely integrates current methodology for data collection, including remote sensing and manual limnological sampling, together with heterogeneous robotic and sensor systems to extend the spatial and temporal sampling. Such big amount of data will be analyzed and processed using ensemble prediction models for determining the development and severity of blooms both in time and space (when and where) and for testing limnological hypotheses. This paper provides insights on open research questions and the methodology used, as well as best practices for interdisciplinary collaboration across different departments, institutions, and citizen scientists.

@inproceedings{QuaEwi+19,
  author = {Alberto Quattrini Li and Holly Ewing and Annie Bourbonnais
            and Paolo Stegagno and Ioannis Rekleitis and
            Denise Bruesewitz and Kathryn Cottingham and
            Devin Balkcom and Mark Ducey and Kenneth Johnson
            and Stephen Licht and David Lutz and Jason M.
            O'Kane and Michael Palace and Christopher Roman
            and V. S. Subrahmanian and Kathleen Weathers},
  booktitle = {Proc. Workshop on Informed Scientific Sampling in Large-
               scale Outdoor Environments \emph{at} IEEE/RSJ
               International Conference on Intelligent
               Robots and Systems},
  title = {Computational methods and autonomous robotics systems for
           modeling and predicting harmful cyanobacterial
           blooms},
  year = {2019}
}