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Integrating random forests and propagation models for high-resolution noise mapping.

Ying Liu1, Tor Oiamo2, Daniel Rainham3

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|February 25, 2021
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A new hybrid model accurately maps total environmental noise, overcoming limitations of traditional methods. This approach combines traffic modeling and machine learning for precise noise level predictions in urban areas.

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Area of Science:

  • Environmental Science
  • Acoustics
  • Geoinformatics

Background:

  • Long-term environmental noise exposure poses health risks.
  • Existing noise mapping methods struggle with complex 3D urban environments.
  • Accurate noise mapping is crucial for urban health planning.

Purpose of the Study:

  • To develop and validate a hybrid approach for high-resolution total environmental noise mapping.
  • To improve upon traditional noise mapping techniques in complex 3D built environments.
  • To assess the accuracy of the hybrid model using ground-based measurements.

Main Methods:

  • A hybrid model combining a traffic propagation model and random forests (RF) machine learning was developed.
  • The model utilized road traffic flow, 3D building data, and digital elevation models for traffic noise prediction.
  • RF models were trained on residuals between predicted traffic noise and measured total noise, incorporating environmental and geographic variables.

Main Results:

  • The hybrid approach achieved high-resolution (30m x 30m) noise mapping for Montreal.
  • Prediction errors for daily average noise levels were low: Mean Error (-0.03 dB(A)), Mean Absolute Error (2.67 dB(A)), and Root Mean Squared Error (3.36 dB(A)).
  • The model successfully integrated deterministic and stochastic components for accurate noise estimation.

Conclusions:

  • The hybrid deterministic-stochastic model offers accurate total environmental noise mapping over large areas.
  • This method overcomes the limitations of traditional noise mapping in complex 3D urban settings.
  • The approach provides a valuable tool for understanding and mitigating noise pollution in cities.