Deep-View Integration of Coastal Observations and Models to Inform Water Quality Resource Managers and Decisions

Computationally intensive artificial intelligence algorithms trained with many sources of observations have the potential to detect impairments to water quality not previously possible through traditional techniques. State agencies in Maryland and Virginia who manage resources in the Chesapeake Bay on the east coast of the United States State currently sample 800 sites around the Chesapeake Bay in boats every month, missing harmful blooms or runoff between their point source observations in space and time. To support their information needs, we exploit multiple satellite data sets of different spatial, spectral, and temporal resolution within our machine learning architecture, entitled ‘deep learning for environmental and ecological prediction, evaluation, and insight with ensembles of water quality’ (DEEP-VIEW). Through fusion of many sources of local and remotely sensed data, we aim to improve predictions of coastal water quality impacted by runoff from land, changes in clarity, harmful blooms, and other indicators needed for resource management of aquaculture, swimming beaches, and boating.