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The hunt for red tides: Deep learning algorithm forecasts shellfish toxicity at site scales in coastal Maine.

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Title: The hunt for red tides: Deep learning algorithm forecasts shellfish toxicity at site scales in coastal Maine.
Authors: Grasso, Isabella1 (AUTHOR), Archer, Stephen D.1 (AUTHOR), Burnell, Craig1 (AUTHOR), Tupper, Benjamin1 (AUTHOR), Rauschenberg, Carlton1 (AUTHOR), Kanwit, Kohl2 (AUTHOR), Record, Nicholas R.1 (AUTHOR) nrecord@bigelow.org
Source: Ecosphere. Dec2019, Vol. 10 Issue 12, pN.PAG-N.PAG. 1p.
Subject Terms: *Shellfish, *Red tide, *Aquaculture, *Algal blooms, Deep learning, Machine learning, Forecasting
Geographic Terms: Maine
Abstract: Farmed and wild harvest shellfish industries are increasingly important components of coastal economies globally. Disruptions caused by harmful algal blooms (HABs), colloquially known as red tides, are likely to worsen with increasing aquaculture production, environmental pressures of coastal development, and climate change, necessitating improved HAB forecasts at finer spatial and temporal resolution. We leveraged a dataset of chemical analytical toxin measurements in coastal Maine to demonstrate a new machine learning approach for high‐resolution forecasting of paralytic shellfish toxin accumulation. The forecast used a deep learning neural network to provide weekly site‐specific forecasts of toxicity levels. The algorithm was trained on images constructed from a chemical fingerprint at each site composed of a series of toxic compound measurements. Under various forecasting configurations, the forecast had high accuracy, generally >95%, and successfully predicted the onset and end of nearly all closure‐level toxic events at the site scale at a one‐week forecast time. Tests of forecast range indicated a decline in accuracy at a three‐week forecast time. Results indicate that combining chemical analytical measurements with new machine learning tools is a promising way to provide reliable forecasts at the spatial and temporal scales useful for management and industry. [ABSTRACT FROM AUTHOR]
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Database: GreenFILE
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Abstract:Farmed and wild harvest shellfish industries are increasingly important components of coastal economies globally. Disruptions caused by harmful algal blooms (HABs), colloquially known as red tides, are likely to worsen with increasing aquaculture production, environmental pressures of coastal development, and climate change, necessitating improved HAB forecasts at finer spatial and temporal resolution. We leveraged a dataset of chemical analytical toxin measurements in coastal Maine to demonstrate a new machine learning approach for high‐resolution forecasting of paralytic shellfish toxin accumulation. The forecast used a deep learning neural network to provide weekly site‐specific forecasts of toxicity levels. The algorithm was trained on images constructed from a chemical fingerprint at each site composed of a series of toxic compound measurements. Under various forecasting configurations, the forecast had high accuracy, generally >95%, and successfully predicted the onset and end of nearly all closure‐level toxic events at the site scale at a one‐week forecast time. Tests of forecast range indicated a decline in accuracy at a three‐week forecast time. Results indicate that combining chemical analytical measurements with new machine learning tools is a promising way to provide reliable forecasts at the spatial and temporal scales useful for management and industry. [ABSTRACT FROM AUTHOR]
ISSN:21508925
DOI:10.1002/ecs2.2960