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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

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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.
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Propagation of Uncertainty from Systematic Error01:10

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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...
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Propagation of Uncertainty from Random Error00:59

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Related Experiment Videos

Explore a Multivariate Bayesian Uncertainty Processor driven by artificial neural networks for probabilistic PM2.5

Yanlai Zhou1, Li-Chiu Chang2, Fi-John Chang3

  • 1Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan; Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, N-0316 Oslo, Norway.

The Science of the Total Environment
|December 9, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel approach using a Multivariate Bayesian Uncertainty Processor (MBUP) with artificial neural networks (ANNs) to improve multi-step-ahead PM2.5 forecasts by reducing predictive uncertainty.

Keywords:
Air qualityArtificial intelligenceBayesian Uncertainty ProcessorProbabilistic forecastTaipei City

Related Experiment Videos

Area of Science:

  • Environmental Science
  • Data Science
  • Atmospheric Science

Background:

  • Accurate forecasting of PM2.5 concentrations is crucial for public health and environmental monitoring.
  • Quantifying predictive uncertainty in complex, nonlinear multivariate air quality models remains a significant challenge.
  • Existing artificial neural network (ANN) models often struggle with inherent uncertainties in multi-step-ahead predictions.

Purpose of the Study:

  • To develop and validate an integrated approach combining a Multivariate Bayesian Uncertainty Processor (MBUP) with ANNs for accurate probabilistic PM2.5 forecasting.
  • To capture the nonlinear multivariate dependence structure between observed and forecasted air quality data.
  • To alleviate the predictive uncertainty associated with ANN-based PM2.5 forecast models.

Main Methods:

  • Integration of an MBUP with an artificial neural network (ANN) for probabilistic PM2.5 forecasting.
  • Utilized the Adaptive Neural Fuzzy Inference System (ANFIS) for deterministic forecasting, outperforming the Back Propagation Neural Network (BPNN).
  • Employed Monte Carlo simulation within Bayesian Uncertainty Processors (BUPs) to model data dependence and generate probabilistic predictive intervals.

Main Results:

  • The ANFIS model demonstrated superior deterministic forecast accuracy compared to BPNN by effectively learning emission mechanisms.
  • The proposed MBUP approach significantly outperformed the Univariate Bayesian Uncertainty Processor (UBUP).
  • MBUP successfully characterized complex nonlinear multivariate dependencies, reducing predictive uncertainty and enhancing forecast reliability.

Conclusions:

  • The integrated MBUP and ANN approach offers a reliable method for reducing predictive uncertainty in PM2.5 forecasting.
  • This method significantly improves the accuracy and reliability of multi-step-ahead PM2.5 concentration predictions.
  • The study highlights the capability of MBUP in handling complex data dependencies for improved air quality modeling.