{"title":"Development of traffic noise prediction model for major arterial roads of tier-II city of India (Surat) using artificial neural network","authors":"Ramesh B. Ranpise, B. Tandel, Vivek A. Singh","doi":"10.1515/noise-2021-0013","DOIUrl":null,"url":null,"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.","PeriodicalId":44086,"journal":{"name":"Noise Mapping","volume":"8 1","pages":"172 - 184"},"PeriodicalIF":1.7000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/noise-2021-0013","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Noise Mapping","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/noise-2021-0013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ACOUSTICS","Score":null,"Total":0}
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.
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.