Publications

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. While this project just started and does not have new result yet, 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.