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Related Concept Videos

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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 particular...
Uncertainty: Overview00:59

Uncertainty: Overview

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.
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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 't,' or...
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

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...
Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...

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Related Experiment Video

Updated: Jul 15, 2026

Measurement of Greenhouse Gas Flux from Agricultural Soils Using Static Chambers
11:50

Measurement of Greenhouse Gas Flux from Agricultural Soils Using Static Chambers

Published on: August 3, 2014

Gated recurrent unit model for forecasting greenhouse gas concentrations with uncertainty quantification.

Erica Hargety Kimei1,2, Devotha Godfrey Nyambo1, Neema Mduma1

  • 1School of Computation and Communication Science and Engineering, The Nelson Mandela African, Institution of Science and Technology, Arusha, Tanzania.

Frontiers in Artificial Intelligence
|July 13, 2026
PubMed
Summary

Accurate farm-level forecasting of greenhouse gases (nitrous oxide, methane, carbon dioxide) from dairy cattle is crucial for climate change mitigation. This study developed an uncertainty-aware deep learning model for precise hourly predictions, integrating diverse data sources for improved accuracy.

Keywords:
GHGGRUIoTforecastinggreenhouse gas concentrationremote sensinguncertainty quantification

Related Experiment Videos

Last Updated: Jul 15, 2026

Measurement of Greenhouse Gas Flux from Agricultural Soils Using Static Chambers
11:50

Measurement of Greenhouse Gas Flux from Agricultural Soils Using Static Chambers

Published on: August 3, 2014

Area of Science:

  • Agricultural Science
  • Environmental Science
  • Data Science

Background:

  • Accurate farm-level forecasting of greenhouse gas (GHG) emissions from dairy cattle is vital for developing effective climate change mitigation strategies and policy planning in livestock systems.
  • Current methods may lack the precision needed for granular, hourly predictions of specific GHGs like nitrous oxide, methane, and carbon dioxide.

Purpose of the Study:

  • To propose and evaluate an uncertainty-aware deep learning model for forecasting hourly concentrations of nitrous oxide, methane, and carbon dioxide from dairy cattle.
  • To integrate multi-source data, including remote sensing and ground-based sensors, for enhanced prediction accuracy.
  • To quantify both epistemic and aleatoric uncertainty in the forecasting model.

Main Methods:

  • Developed a causal multivariate time series model with a 24-h lookback window for one-step-ahead prediction.
  • Integrated data from remote sensing and ground-based sensors.
  • Employed a dual-output gated recurrent unit for mean and variance estimation, alongside Monte Carlo dropout and Gaussian Negative Log-Likelihood for uncertainty quantification.
  • Utilized a two-stage evaluation protocol including rolling cross-validation and holdout testing.

Main Results:

  • The model demonstrated stable generalization across temporal folds and strong probabilistic calibration, with empirical 95% coverage between 93.6% and 94.8% on the test set.
  • Feature selection identified rainfall, normalized difference vegetation index, humidity, temperature, trend, and season as key predictors.
  • Incorporating exogenous variables significantly improved model performance.

Conclusions:

  • The proposed uncertainty-aware deep learning framework provides a proof of concept for accurate GHG forecasting in controlled zero-grazing dairy systems.
  • The model's ability to quantify uncertainty enhances its reliability for climate change mitigation policy planning.
  • Potential exists for broader application in diverse livestock systems following multi-site validation.