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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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相关实验视频

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增强环境数据归算:一个物理限制的机器学习框架.

Marcos Pastorini1, Rafael Rodríguez2, Lorena Etcheverry1

  • 1Department of Computer Science, School of Engineering, Universidad de la República, Herreira y Reissig, 565, Montevideo 11300, Uruguay.

The Science of the total environment
|March 24, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种机器学习框架,以填补缺少的环境数据,提高流域模型的准确性. 该方法有效地归因于气象,水量和质量数据,减少水资源管理中的不确定性.

关键词:
数据归算数据的归算方法环境数据 环境数据机器学习是机器学习.缺失的值是指缺失的值.物理限制 物理限制

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科学领域:

  • 环境科学 环境科学
  • 管理水资源 管理水资源
  • 机器学习 机器学习

背景情况:

  • 综合流域模型对于分析气候和水资源至关重要.
  • 有限的现场数据给这些模型带来了显著的不确定性.
  • 在收集更多数据之前,利用现有数据是改善模型性能的关键.

研究的目的:

  • 开发一种新的机器学习框架,用于归因缺少的环境数据.
  • 评估框架在处理不同领域数据缺口方面的有效性.
  • 通过数据增强来提高综合环境模型的性能.

主要方法:

  • 开发了一个包含物理约束的机器学习框架.
  • 该框架被应用在气象学,水量和水质方面的缺失数据上.
  • 模型性能使用纳什-萨特克利夫效率 (NSE) 度量来评估.

主要成果:

  • 该框架成功地将环境领域中高比例的缺失数据归因于环境领域.
  • 获得了令人满意的归算结果,气象学最低NSE值为0.72,水文学变量为>0.97.
  • 超过78%的物理水质变量和66%的化学水质变量分别显示NSE>0.45和>0.35.

结论:

  • 拟议的机器学习框架对于环境建模中的数据增强是有效的.
  • 输入缺失数据显著提高了性能,并减少了综合流域模型中的不确定性.
  • 这种方法提供了一个有价值的工具,通过增强数据可用性来优化水资源管理.