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相关概念视频

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
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...
43
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

55
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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相关实验视频

Updated: Jul 4, 2025

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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为黑碳度构建可转移和可解释的机器学习模型.

Pak Lun Fung1, Marjan Savadkoohi2, Martha Arbayani Zaidan3

  • 1Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, Helsinki FI-00560, Finland; Helsinki Institute of Sustainability Science, Faculty of Science, University of Helsinki, Helsinki FI-00560, Finland.

Environment international
|January 29, 2024
PubMed
概括

机器学习模型可以估计黑碳 (BC) 度,作为虚拟传感器. 这些可解释的模型显示出在不同欧洲城市和交通场所之间有前途的可转移性.

关键词:
估计BC的BC估计神经网络的神经网络相对重要性 相对重要性这就是 SHAP SHAP 的意思.交通排放的排放量.虚拟传感器 虚拟传感器

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科学领域:

  • 环境科学 环境科学
  • 数据科学数据科学数据科学
  • 大气化学 大气化学

背景情况:

  • 碳黑 (BC) 构成健康风险,但缺乏广泛的现场监测.
  • 机器学习 (ML) 为空气质量数据提供了虚拟传感器的解决方案.
  • 本研究侧重于用于BC估计的ML模型的可转移性和可解释性.

研究的目的:

  • 评估和比较白盒 (WB) 和黑盒 (BB) ML模型来估计BC度.
  • 评估在一个城市背景站点训练的ML模型的可转移性到其他欧洲城市和交通站点.
  • 分析不同ML模型的可解释性,以量化BC估计的特征重要性.

主要方法:

  • 使用巴塞罗那的长期空气污染物和天气数据训练和测试多个WB和BBML模型.
  • 在赫尔辛基 (交通) 和德累斯顿 (城市背景) 站点评估模型性能.
  • 采用各种解释技术来量化每个模型的特征重要性.

主要成果:

  • 黑碳 (BC) 与积累模式的粒子数度 (PNacc) 和二氧化 (NO2) 强烈相关.
  • 在巴塞罗那接受训练的ML模型在赫尔辛基和德累斯顿表现出色,表明了良好的可转移性.
  • 黑盒模型,特别是长期短期记忆 (LSTM) 模型,在解释BC变异性方面通常优于白盒模型.

结论:

  • 可解释的ML模型可以有效地在不同的地理位置和地点类型 (城市背景,交通) 中转移.
  • PNacc和NO2是关键的预测因素,但它们的影响可以是积极的或消极的,取决于网站.
  • 该研究强调了可转移和可解释的ML模型在增强空气质量监测网络方面的潜力.