当前位置: X-MOL 学术J. Great Lakes Res. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Using machine learning to model and predict water clarity in the Great Lakes
Journal of Great Lakes Research ( IF 2.2 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.jglr.2020.07.022
Cameron C. Lee , Brian B. Barnes , Scott C. Sheridan , Erik T. Smith , Chuanmin Hu , Douglas E. Pirhalla , Varis Ransibrahmanakul , Ryan Adams

Abstract 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.

中文翻译:

使用机器学习来建模和预测五大湖的水透明度

摘要 在过去的几十年里,多种环境问题导致五大湖的水清澈度发生了巨大变化。虽然许多关键因素众所周知并且具有直接的人为起源,但气候变异和变化也会影响不同时间尺度的水透明度,但它们的影响很少被研究。基于最近对天气尺度天气模式与水清晰度之间的单变量关系的检查,本研究利用具有外源输入的非线性自回归模型(NARX 模型)来探索多变量气候与水清晰度的关系。在观测期(1997-2016 年)训练的模型被外推回 1979 年以重建每日尺度的历史水清晰度数据集,并在重新预测模式下用于估计实时预测技能。在检查的 20 个地区中,模型在密歇根湖和休伦湖表现最好,尤其是在春季和夏季。NARX 模型在几乎所有地区都比简单的持久性模型和季节性趋势模型表现更好,这表明气候变率是导致水透明度波动的一个因素。此外,20 个地区中的 6 个也显示出对至少 1 周提前期的有用预测的前景,其中三个地区显示出提前两个月的技能。
更新日期:2020-12-01
down
wechat
bug