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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]
Copyright of Ecosphere is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: The hunt for red tides: Deep learning algorithm forecasts shellfish toxicity at site scales in coastal Maine.
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  Data: <searchLink fieldCode="JN" term="%22Ecosphere%22">Ecosphere</searchLink>. Dec2019, Vol. 10 Issue 12, pN.PAG-N.PAG. 1p.
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  Data: *<searchLink fieldCode="DE" term="%22Shellfish%22">Shellfish</searchLink><br />*<searchLink fieldCode="DE" term="%22Red+tide%22">Red tide</searchLink><br />*<searchLink fieldCode="DE" term="%22Aquaculture%22">Aquaculture</searchLink><br />*<searchLink fieldCode="DE" term="%22Algal+blooms%22">Algal blooms</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink>
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  Label: Abstract
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  Data: 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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  Label:
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  Data: <i>Copyright of Ecosphere is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/ecs2.2960
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
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      – SubjectFull: Shellfish
        Type: general
      – SubjectFull: Red tide
        Type: general
      – SubjectFull: Aquaculture
        Type: general
      – SubjectFull: Algal blooms
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Maine
        Type: general
    Titles:
      – TitleFull: The hunt for red tides: Deep learning algorithm forecasts shellfish toxicity at site scales in coastal Maine.
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            – D: 01
              M: 12
              Text: Dec2019
              Type: published
              Y: 2019
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