Analysis of High-Precision Noise Prediction and Modeling Applications in Environmental Management: SoundPLAN Urban Noise Prediction Software Brings Innovative Solutions!

Introduction

In recent years, with the rapid development of noise simulation software and machine learning technology, high-precision noise prediction and modeling technology has quietly changed the way we deal with noise pollution, becoming an effective tool in environmental governance.

01 Innovation Path of Noise Prediction Technology

Traditional noise monitoring methods have limitations and struggle to obtain large amounts of detailed noise data. In recent years, the emergence of the rotating mobile monitoring method (also known as the repeated fixed monitoring method) has solved this problem. This method combines the advantages of fixed and mobile monitoring, significantly expanding the spatial coverage and temporal resolution of noise data.

2023 In the year, the research team from North China University of Technology conducted monitoring activities covering Haidian District, Beijing, 54.79 kilometers of roads and 22.15 square kilometers of total area, from 152 fixed sampling points collected 18213 units of 1 second interval of A weighted equivalent noise measurements. This multi-sensor fusion acquisition mode laid the data foundation for high-precision noise prediction.

02 SoundPLAN: The Core Tool of Noise Prediction Technology

In the field of noise prediction software, Germany's SoundPLAN noise simulation software is highly recognized by professionals worldwide. After 30 years of development, it has now released 9.1 versions. SoundPLAN The software has a modular structure, allowing users to purchase the modules they need according to their requirements. It can perform various international standard road, railway, industrial noise simulation predictions, sound barrier optimization design, noise assessment inside and outside factories, as well as air pollution assessment. The latest version of SoundPLANnoise 9.1 incorporates 2024 the new version of the standard from the year ISO 9613-2 for calculating sound attenuation during outdoor propagation. The software also considers various types of measurement objects, including cylinders, stack directionality, and attenuation in forest areas.

03 Machine Learning Empowering Noise Prediction

Research on noise prediction based on big data and machine learning methods has made significant progress. The research team from North China University of Technology trained six machine learning models and a linear regression model to predict traffic noise. The study showed that the random forest model performed the best ( R2 = 0.72 ， RMSE = 3.28 dB ), followed by K- the nearest neighbor regression model ( R2 = 0.66 ， RMSE = 3.43 dB ). The best random forest model identified the distance to main roads, street green view index, and the maximum visible proportion of cars within the past 3 seconds as the top three contributing factors. These findings provide a scientific basis for noise control.

04 Practical Applications of Noise Prediction Technology

Noise prediction technology has wide applications in environmental governance. Luohu District in Shenzhen used SoundPLAN software to generalize and model the city's main roads and surrounding features, and adjusted parameters based on actual monitoring data to create a noise distribution map of the main roads in Luohu District.

When addressing noise pollution from urban elevated composite roads, planners used SoundPLAN software to simulate the current noise pollution and the noise reduction effect after installing sound barriers. This simulation and prediction capability allows decision-makers to anticipate noise impacts before project construction and formulate corresponding mitigation measures.

Noise prediction for substations also benefits from this technology. A study based on SoundPLAN software showed that under unchanged model environmental parameters and sound field obstacles, the measurement point back-calculated source prediction results were closer to actual measurements than the recommended source prediction.

05 Future Development Trends and Challenges

With technological advances, noise prediction software functions continue to improve. However, noise prediction still faces challenges. How to balance computational accuracy and efficiency, how to handle multiple reflections and occlusion effects in complex urban environments, and how to effectively translate prediction results into governance measures are issues requiring further research.

With the continuous iteration of SoundPLAN and other software, and the ongoing optimization of machine learning algorithms, high-precision noise maps will move from research to widespread use. Urban planners will be able to simulate acoustic environments more accurately, design more effective noise prevention and control measures, and create more livable urban sound environments for residents. The improved accuracy of noise prediction models enables decision-makers to anticipate noise impacts before project construction and formulate corresponding mitigation measures, thereby reducing governance costs and improving environmental governance effectiveness.