The Application of Neural Networks to the Prediction of Traffic Noise

Wu, Shuoxian; Jiping, Zhang

doi:10.20855/ijav.2000.5.465

Cited by 3 publications

(1 citation statement)

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“…Based on the traffic noise data measured near a highway in China, an artificial neural network was formed to predict further traffic noise levels. It was found that the predicted data by the ANN model were in good agreement with measured data and it was concluded that an artificial neural network provides a new method for traffic noise prediction [24]. The position of the measurement station, the geographical situation between the noise source and the measurement station, wind speed and direction, air temperature and relative humidity, and time of day, [25] were considered in artificial neural networks to model the variation of noise levels from traffic around campus area.…”

Section: Reviewmentioning

confidence: 77%

Road Traffic Noise Prediction with Neural Networks - A Review

Kumar

Parida

Katiyar

2011

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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Abstract. This paper aims to summarize the findings of research concerning the application of neural networks in traffic noise prediction. Modeling and prediction of traffic noise by means of classical approaches is a very complex and nonlinear process, due to involvement of several factors on which noise level depends. To overcome these problems, researchers and acoustical engineers have applied the artificial neural network in the field of traffic noise prediction. After a critical review of various neural network based models developed for road traffic noise prediction cited in the literature it was concluded that ANN based models were capable of predicting traffic noise more accurately and effectively as compared to deterministic and statistical models.

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Section: Reviewmentioning

confidence: 77%

Road Traffic Noise Prediction with Neural Networks - A Review

Kumar

Parida

Katiyar

2011

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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Urban indices as environmental noise indicators

Souza

Giunta

2011

Computers, Environment and Urban Systems

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Development of an Empirical Model for Noise Prediction in Typical Industrial Workrooms Based on Artificial Neural Networks

Aliabadi

Golmohammadi

Mansoorizadeh

et al. 2013

Noise & Vibration Worldwide

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Noise prediction techniques can be employed as practical tools for evaluating the cost-effectiveness of acoustic treatments and consequently, prevent blind treatments by experts so that more acceptable conditions are obtained. One of the most important issues in this regard is the development of accurate models for analysis of the complex relationships among acoustic features affecting noise level in workrooms. In this study, artificial neural networks were employed to develop a relatively accurate model for noise prediction in noisy industrial workrooms. The data from nine acoustic, structural and embroidery process features influencing the noise in 60 embroidery workrooms was used to develop the noise prediction techniques. Multilayer feed forward neural networks with different structures were developed by using MATLAB. The best neural networks could accurately predict the noise level (RMSE=0.69 dB and R2=0.88). Although networks are empirical in nature, the results confirmed the potential of this approach for minimizing the uncertainties in acoustics modeling. This model gives professionals the opportunity to make an optimum decision about the effectiveness of acoustic treatment scenarios in workrooms.

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The Application of Neural Networks to the Prediction of Traffic Noise

Cited by 3 publications

References 0 publications

Road Traffic Noise Prediction with Neural Networks - A Review

Road Traffic Noise Prediction with Neural Networks - A Review

Urban indices as environmental noise indicators

Development of an Empirical Model for Noise Prediction in Typical Industrial Workrooms Based on Artificial Neural Networks

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