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Using machine learning to model and predict water clarity in the Great Lakes
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2020
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Source: Journal of Great Lakes Research, 46(6), 1501-1510
[PDF-5.58 MB]
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Journal Title:Journal of Great Lakes Research
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Description:Over the last several decades, multiple environmental issues have led to dramatic changes in the water clarity of the Great Lakes. While many of the key factors are well-known and have direct anthropogenic origins, climatic variability and change can also impact water clarity at various temporal scales, but their influence is less often studied. Building upon a recent examination of the univariate relationships between synoptic-scale weather patterns and water clarity, this research utilizes nonlinear autoregressive models with exogenous input (NARX models) to explore the multivariate climate-to-water clarity relationship. Models trained on the observation period (1997–2016) are extrapolated back to 1979 to reconstruct a daily-scale historical water clarity dataset, and used in a reforecast mode to estimate real-time forecast skill. Of the 20 regions examined, models perform best in Lakes Michigan and Huron, especially in spring and summer. The NARX models perform better than a simple persistence model and a seasonal-trend model in nearly all regions, indicating that climate variability is a contributing factor to fluctuations in water clarity. Further, six of the 20 regions also show promise of useful forecasts to at least 1 week of lead-time, with three of those regions showing skill out to two months of lead time.
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Source:Journal of Great Lakes Research, 46(6), 1501-1510
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ISSN:0380-1330
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Rights Information:Accepted Manuscript
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Rights Statement:This manuscript is made available under the Elsevier user license https://www.elsevier.com/open-access/userlicense/1.0/
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