Civil Engineering Department, DCRUST Murthal, Haryana, India.
Environ Monit Assess. 2024 Jan 18;196(2):168. doi: 10.1007/s10661-023-12285-4.
Noise pollution is one of the negative consequences of growth and development in cities. Traffic noise pollution due to traffic growth is the main aspect that worsens city quality of life. Therefore, research around the world is being conducted to manage and reduce traffic noise. A number of traffic noise prediction models have been proposed employing fixed effect modelling approach considering each observation as independent; however, observations may have spatial and temporal correlations and unobserved heterogeneity. Random effect models overcome these problems. This study attempts to develop a random effect generalized linear model (REGLM) along with a machine learning random forest (RF) model to validate the results, concerning the parameters related to road, traffic and environmental conditions. Models were developed based on the experimental quantities in Delhi in year 2022-2023. Both the models performed comparably well in terms of coefficient of determination. Random forest models with R= 0.75, whereas random effect generalized linear model had an R= 0.70. REGLM model has the ability to quantify the effects of explanatory variables over traffic noise pollution and will be more helpful in prioritizing of resources and chalking out control strategies.
噪声污染是城市发展带来的负面影响之一。由于交通增长而导致的交通噪声污染是恶化城市生活质量的主要方面。因此，世界各地都在开展研究，以管理和减少交通噪声。已经提出了许多交通噪声预测模型，采用固定效应建模方法，将每个观测值视为独立的；然而，观测值可能具有空间和时间相关性以及未观察到的异质性。随机效应模型可以克服这些问题。本研究试图开发一个随机效应广义线性模型（REGLM）以及一个机器学习随机森林（RF）模型，以验证与道路、交通和环境条件相关的参数的结果。模型是基于 2022-2023 年在德里的实验数据开发的。在确定系数方面，这两种模型的表现都相当不错。随机森林模型的 R=0.75，而随机效应广义线性模型的 R=0.70。REGLM 模型能够量化解释变量对交通噪声污染的影响，这将有助于优先考虑资源和制定控制策略。
