Development of traffic noise prediction model for major arterial roads of tier-II city of India (Surat) using artificial neural network

IF 1.7 Q2 ACOUSTICS Noise Mapping Pub Date : 2021-01-01 DOI:10.1515/noise-2021-0013
Ramesh B. Ranpise, B. Tandel, Vivek A. Singh
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引用次数: 11

Abstract

Abstract In the issue of expanding noise levels the world over, road traffic noise is main contributor. The investigation of street traffic noise in urban communities is a significant issue. Ample opportunity has already passed to understand the significance of noise appraisal through prediction models with the goal that assurance against street traffic noise can be actualized. Noise predictions models are utilized in an increasing range of decision-making applications. This study’s main objective is to assess ambient noise levels at major arterial roads of Surat city, compare these with prescribed standards, and develop a noise prediction model for arterial roads using an Artificial Neural Network. The feed-forward back propagation method has been used to train the model. Models have been developed using the data of three roads separately, and one final model has also been developed using the data of all three roads. Among the prediction in three arterial roads, the predicted output result from the model of Adajan-Rander showed a better correlation with a mean squared error (MSE) of 0.789 and R2 value of 0.707. But with the combined model, there is a slight deterioration in mean squared value (MSE) 1.550, with R2 not getting changed much significantly, i.e., 0.755. However, the combined model’s prediction can be adopted due to the variety of data used in its training.
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基于人工神经网络的印度苏拉特二线城市主干道交通噪声预测模型的建立
摘要在世界范围内噪声水平不断扩大的问题上,道路交通噪声是主要因素。城市社区道路交通噪声调查是一个重要课题。通过预测模型来理解噪声评估的重要性,以实现对街道交通噪声的保证,已经有了充分的机会。噪声预测模型被用于越来越多的决策应用中。本研究的主要目的是评估苏拉特市主要干线道路的环境噪声水平,将其与规定标准进行比较,并使用人工神经网络开发干线道路的噪声预测模型。使用前馈-反向传播方法来训练模型。分别使用三条道路的数据开发了模型,还使用所有三条道路数据开发了一个最终模型。在三条主干道的预测中,Adajan Rander模型的预测输出结果显示出更好的相关性,均方误差(MSE)为0.789,R2值为0.707。但对于组合模型,均方值(MSE)1.550略有恶化,R2变化不大,即0.755。然而,由于训练中使用的数据种类繁多,可以采用组合模型的预测。
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来源期刊
Noise Mapping
Noise Mapping ACOUSTICS-
CiteScore
7.80
自引率
17.90%
发文量
5
审稿时长
12 weeks
期刊介绍: Ever since its inception, Noise Mapping has been offering fast and comprehensive peer-review, while featuring prominent researchers among its Advisory Board. As a result, the journal is set to acquire a growing reputation as the main publication in the field of noise mapping, thus leading to a significant Impact Factor. The journal aims to promote and disseminate knowledge on noise mapping through the publication of high quality peer-reviewed papers focusing on the following aspects: noise mapping and noise action plans: case studies; models and algorithms for source characterization and outdoor sound propagation: proposals, applications, comparisons, round robin tests; local, national and international policies and good practices for noise mapping, planning, management and control; evaluation of noise mitigation actions; evaluation of environmental noise exposure; actions and communications to increase public awareness of environmental noise issues; outdoor soundscape studies and mapping; classification, evaluation and preservation of quiet areas.
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