期刊
ECOSPHERE
卷 10, 期 12, 页码 -出版社
WILEY
DOI: 10.1002/ecs2.2960
关键词
forecast; harmful algal bloom; Maine; neural network; paralytic shellfish toxin
类别
资金
- National Science Foundation Research Experience for Undergraduates site at Bigelow Laboratory [1460861]
- National Atmospheric and Space Administration [NNX16AG59G]
- National Atmospheric and Space Administration (EPSCoR award) [EP-20-01]
- Bigelow Laboratory institutional funds
- Directorate For Geosciences
- Division Of Earth Sciences [1460861] Funding Source: National Science Foundation
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据