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相关概念视频

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

475
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
475
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

27
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...
27
Uncertainty: Overview00:59

Uncertainty: Overview

516
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
516

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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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基于不确定性分析和多模型整合的微粒物质2.5度预测系统.

Yamei Chen1, Jianzhou Wang1, Runze Li1

  • 1Institute of Systems Engineering, Macau University of Science and Technology, 999078, Macao.

The Science of the total environment
|December 10, 2024
PubMed
概括

这项研究引入了一种先进的空气污染预警系统,以应对健康威胁. 新的框架提高了预测准确度,并为可靠的颗粒物 (PM2.5) 预测量了不确定性.

科学领域:

  • 环境科学 环境科学
  • 公共卫生 公共卫生
  • 数据科学数据科学数据科学

背景情况:

  • 空气污染对全球公共卫生构成重大风险.
  • 现有的早期预警系统在数据噪声和不确定性估计方面扎.
  • 准确预测污染物度对于缓解和可持续发展至关重要.

研究的目的:

  • 开发一个强大的空气污染度预警系统.
  • 解决现有模型在数据噪声和不确定性方面的局限性.
  • 为决策者提供可靠的PM2.5度预测.

主要方法:

  • 一个新的预测框架,结合了分解策略和元启发式优化.
  • 数据预处理,包括横向消除噪音和垂直颗粒.
  • 使用间隔概率的确定性预测与不确定性分析的整合.

主要成果:

  • 拟议的系统通过消除噪音和颗粒化,显著降低了数据的复杂性.
  • 优化算法可以提高预测准确性和模型概括性.
  • 成功量化了PM2.5预测中的不确定性.

结论:

关键词:
大气污染预测大气污染预测模糊的战略 模糊的战略整合框架 整合框架优化算法优化算法不确定性分析不确定性分析

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  • 综合系统为空气污染预测提供了科学和可靠的方法.
  • 与基准模型相比,在预测准确度方面取得了明显的改进 (68.12%的APE减少,68.88%的RMSE减少).
  • 为有效的空气污染控制战略提供关键的技术支持.