Comparisons between physics-based, engineering, and statistical learning models for outdoor sound propagation | JoVE Visualize

Area of Science:

Acoustics
Environmental Science
Computational Modeling

Background:

Outdoor sound propagation modeling is crucial for noise assessment and environmental planning.
Existing models range from complex physics-based simulations to simplified engineering methods and statistical learning approaches.
A need exists to evaluate and compare the accuracy of these diverse modeling techniques.

Purpose of the Study:

To benchmark and compare the predictive accuracy of various outdoor sound propagation models.
To evaluate engineering models (ISO 9613-2, Harmonoise, Nord2000) against a physics-based Crank-Nicholson parabolic equation (CNPE) model.
To assess the performance of statistical learning models (bagged decision tree, random forest, boosting, artificial neural network) using CNPE as a reference.

Main Methods:

A physics-based Crank-Nicholson parabolic equation (CNPE) model was used as a benchmark to generate simulated sound propagation data.
Simulated data encompassed diverse conditions: downward/upward refraction, hard/soft boundaries, and low frequencies.
Engineering models (ISO 9613-2, Harmonoise, Nord2000) and statistical learning models were compared against CNPE predictions using skill scores.

Main Results:

Statistical learning models demonstrated superior performance, with skill scores consistently above 99.5% (bagged decision tree, random forest, boosting, artificial neural network).
Engineering models showed varied performance: Nord2000 achieved 83.8%, while ISO 9613-2 (0.6%) and Harmonoise (-7.1%) had significantly lower skill scores.
The study highlights the high accuracy of statistical learning methods in simulating complex sound propagation scenarios.

Conclusions:

Statistical learning models offer a highly accurate and flexible approach for outdoor sound propagation prediction.
These advanced models significantly surpass traditional engineering methods in predictive performance across various acoustic conditions.
The findings support the adoption of machine learning techniques for more reliable environmental noise modeling and assessment.