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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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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
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Related Experiment Video

Updated: Jul 5, 2025

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Random effect generalized linear model-based predictive modelling of traffic noise.

Suman Mann1, Gyanendra Singh2

  • 1Civil Engineering Department, DCRUST Murthal, Haryana, India. suman.mann@dseu.ac.in.

Environmental Monitoring and Assessment
|January 18, 2024
PubMed
Summary
This summary is machine-generated.

Traffic noise pollution negatively impacts urban quality of life. This study developed random effect generalized linear models (REGLM) and random forest (RF) models to predict traffic noise, offering better urban planning strategies.

Keywords:
Machine learningNoise prediction modelsRandom effect generalized linear model (REGLM)Random forest modelTraffic noise pollution

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Area of Science:

  • Environmental Science
  • Urban Planning
  • Statistical Modeling

Background:

  • Urban growth and development lead to increased noise pollution, primarily from traffic.
  • Traffic noise significantly degrades the quality of life in cities.
  • Existing traffic noise prediction models often overlook spatial, temporal correlations, and unobserved heterogeneity.

Purpose of the Study:

  • To develop and compare a random effect generalized linear model (REGLM) and a machine learning random forest (RF) model for traffic noise prediction.
  • To validate model performance using experimental data from Delhi (2022-2023).
  • To identify key road, traffic, and environmental factors influencing traffic noise pollution.

Main Methods:

  • Development of a random effect generalized linear model (REGLM) to account for data dependencies.
  • Implementation of a machine learning random forest (RF) model for comparative analysis.
  • Model calibration and validation using empirical data collected in Delhi.

Main Results:

  • Both REGLM and RF models demonstrated comparable performance in predicting traffic noise.
  • The random forest model achieved a coefficient of determination (R²) of 0.75.
  • The random effect generalized linear model achieved a coefficient of determination (R²) of 0.70.

Conclusions:

  • The REGLM model effectively quantifies the impact of various explanatory variables on traffic noise pollution.
  • Findings support the use of REGLM for prioritizing resource allocation and developing effective traffic noise control strategies.
  • Both models provide valuable insights for urban noise management and planning.