A hybrid predictive model for acoustic noise in urban areas based on time series analysis and artificial neural network

Guarnaccia, Claudio; Quartieri, J.; Tepedino, Carmine

doi:10.1063/1.4982009

Cited by 4 publications

(3 citation statements)

References 22 publications

(26 reference statements)

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“…Vehicles are predominantly sources of low and medium frequency noise, which has a high penetrating power and propagates with low dissipative absorption over long distances. The continuous growth of the car fleet has progressively increased the need for special attention to urban traffic noise, which not only increases in line with the growth of residential, industrial and commercial areas, but also causes adverse impact of noise emissions on people [7], [8]. Predicting the level of noise produced by urban transport is an essential aspect of mitigating environmental pollution.…”

Section: Introductionmentioning

confidence: 99%

Noise estimation using an artificial neural network in the urban area of Jaen, Cajamarca

et al. 2023

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Jaen is a city in constant urban growth which generates an increase in vehicular traffic and active noise pollution. The research presents the development of an artificial neural network (ANN) to estimate the noise produced by vehicular traffic in the urban area of the city. Consequently, information was collected from two investigations coded as T1 and T2, for which a matrix of 10 variables was elaborated with 210 and 273 data respectively. Random random sampling was performed to divide the data matrix into 80% (training) and 20% (validation). Weka software and the multi-layer perceptron (MLP) training algorithm were used to model the ANN. An ANN for T1 with 6-19-1 architecture and an ANN for T2 with 6-15-1 architecture were obtained. The performance of the ANNs was evaluated using the correlation coefficient (R), coefficient of determination (R2) and root mean square error (RMSE). The results show that the MLP networks are able to estimate the sound pressure level with values of R=0.9927, R2=0.9854 and RMSE=0.7313 for T1, R=0.9989, R2=0.9978, and RMSE=0.1515 for T2.

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Section: Introductionmentioning

confidence: 99%

Noise estimation using an artificial neural network in the urban area of Jaen, Cajamarca

et al. 2023

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“…In most recent studies, hybrid models combining systems of ANN and fuzzy interference systems (FIS) (i.e., adaptive neuro fuzzy inference system-ANFIS) have been developed too [28]. Other hybrid models are represented by the integration between time series analysis techniques and ANN [29].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction

et al. 2021

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A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. Leq A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models.

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“…In order to have accurate estimation, along with statistical approaches, the nonlinear methods such as neural networks are suggested. A large number of scholars have applied the combination of statistical methods and neural networks rather than using traditional statistical methods or neural networks separately [21][22][23][24]. A majority of these researchers have found that the hybrid model could achieve more accurate results with higher R 2 (coefficient of determination) and lower level of error indexes, such as RMSE and MSE simultaneously [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Influential Factors on Eating Behaviors: A Hybrid Model of Structural Equation Modelling-Artificial Neural Networks

Kheirollahpour

Danaee

Merican

et al. 2020

The Scientific World Journal

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The importance of eating behavior risk factors in the primary prevention of obesity has been established. Researchers mostly use the linear model to determine associations among these risk factors. However, in reality, the presence of nonlinearity among these factors causes a bias in the prediction models. The aim of this study was to explore the potential of a hybrid model to predict the eating behaviors. The hybrid model of structural equation modelling (SEM) and artificial neural networks (ANN) was applied to evaluate the prediction model. The SEM analysis was used to check the relationship of the emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) and their effect on different categories of eating behavior patterns (EBP). In the second step, the input and output required for ANN analysis were obtained from SEM analysis and were applied in the neural network model. 340 university students participated in this study. The hybrid model (SEM-ANN) was conducted using multilayer perceptron (MLP) with feed-forward network topology. Moreover, Levenberg–Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The tangent/sigmoid function was used for the input layer, while the linear function was applied for the output layer. The coefficient of determination (R2) and mean square error (MSE) were calculated. Using the hybrid model, the optimal network happened at MLP 3-17-8. It was proved that the hybrid model was superior to SEM methods because the R2 of the model was increased by 27%, while the MSE was decreased by 9.6%. Moreover, it was found that BSC, BAS, and EES significantly affected healthy and unhealthy eating behavior patterns. Thus, a hybrid approach could be suggested as a significant methodological contribution from a machine learning standpoint, and it can be implemented as software to predict models with the highest accuracy.

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A hybrid predictive model for acoustic noise in urban areas based on time series analysis and artificial neural network

Cited by 4 publications

References 22 publications

Noise estimation using an artificial neural network in the urban area of Jaen, Cajamarca

Noise estimation using an artificial neural network in the urban area of Jaen, Cajamarca

Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction

Prediction of the Influential Factors on Eating Behaviors: A Hybrid Model of Structural Equation Modelling-Artificial Neural Networks

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