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Automated identification and assessment of environmental noise sources. Heliyon 2023; 9:e12846. [PMID: 36685460 PMCID: PMC9852937 DOI: 10.1016/j.heliyon.2023.e12846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
Noise pollution is one of the major health risks in urban life. The approach to measurement and identification of noise sources needs to be improved and enhanced to reduce high costs. Long measurement times and the need for expensive equipment and trained personnel must be automated. Simplifying the identification of main noise sources and excluding residual and background noise allows more effective measures. By spatially filtering the acoustic scene and combining unsupervised learning with psychoacoustic features, this paper presents a prototype system capable of automated calculation of the contribution of individual noise sources to the total noise level. Pilot measurements were performed at three different locations in the city of Ljubljana, Slovenia. Equivalent sound pressure levels obtained with the device were compared to the results obtained by manually marking individual parts of each of the three measurements. The proposed approach correctly identified the main noise sources in the vicinity of the measurement points.
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Debnath A, Singh PK, Banerjee S. Vehicular traffic noise modelling of urban area-a contouring and artificial neural network based approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:39948-39972. [PMID: 35112254 DOI: 10.1007/s11356-021-17577-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 11/12/2021] [Indexed: 06/14/2023]
Abstract
Road traffic vehicular noise is one of the main sources of environmental pollution in urban areas of India. Also, steadily increasing urbanization, industrialization, infrastructures around city condition causes health risks among the urban populations. In this study, we have explored noise descriptors (L10, L90, Ldn, LNI, TNI, NC), contour plotting and find the suitability of artificial neural networks (ANN) for the prediction of traffic noise all around the Dhanbad township in 15 monitoring stations. In order to develop the prediction model, measuring noise levels of five different hours, speed of vehicles, and traffic volume in every monitoring point have been studied and analyzed. Traffic volume, percent of heavy vehicles, speed, traffic flow, road gradient, pavement, road side carriageway distance factors were taken as input parameter, whereas LAeq as output parameter for formation of neural network architecture. As traffic flow is heterogenous which mainly contains 59%, two wheelers and different vehicle specifications with varying speeds also affect driving and honking behavior which constantly changing noise characteristics. From radial noise diagrams shown that average noise levels of all the stations beyond permissible limit and the highest noise levels were found at the speed of 50-55 km/h in both peak and non-peak hours. Noise descriptors clearly indicate high annoyance level in the study area. Artificial neural network with 7-7-5 formation has been developed and found as optimum due to its sum of square and overall relative error 0.858 and .029 in training and 0.458 and 0.862 in testing phase respectively. Comparative analysis between observed and predicted noise level shows very less deviation up to ± 0.6 dB(A) and the R2 linear values are more than 0.9 in all five noise hours indicating the accuracy of model. Also, it can be concluded that ANN approach is much superior in prediction of traffic noise level to any other statistical method.
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Affiliation(s)
- Abhijit Debnath
- Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, India.
- Department of Environmental Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, India.
| | - Prasoon Kumar Singh
- Department of Environmental Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, India
| | - Sushmita Banerjee
- School of Basic Sciences and Research, Department of Environmental Sciences, Sharda University, Greater Noida, India
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Gilani TA, Mir MS. Modelling road traffic Noise under heterogeneous traffic conditions using the graph-theoretic approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:36651-36668. [PMID: 33704641 PMCID: PMC7947378 DOI: 10.1007/s11356-021-13328-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 03/03/2021] [Indexed: 05/25/2023]
Abstract
A traffic noise system involves several subsystems like road traffic subsystem, human subsystem, environment subsystem, traffic network subsystem, and urban prosperity subsystem. The study's main aim was to develop road traffic noise models using a graph theory approach involving the parameters related to road traffic subsystem. The road traffic subsystem variables selected for the modeling purposes included vehicular speed, traffic volume, carriageway width, number of heavy vehicles, and number of honking events. The interaction of the selected variables considered in the form of permanent noise function is given in the matrix form. Eigenvalues and corresponding eigenvectors are calculated for removing any human judgmental error. The permanent noise function matrix was then updated using the eigenvectors, which was ultimately utilized for obtaining the permanent noise index. Data regarding the selected variables were collected for three months, and the noise parameters included in the study were equivalent noise level (Leq,1h), maximum noise level (L10,1h), and background noise level (L90,1h). A logarithmic transformation was applied to the permanent noise index and linear regression models were developed for Leq,1h , L90,1h , and L10,1h respectively. The models were validated using the data collected from the same locations for nine months. The models were found to provide satisfactory results, although the results were somewhat overestimated. The method can prove beneficial for estimating future noise levels, given the expected changes in values for the independent variables considered in the study.
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Affiliation(s)
- Towseef Ahmed Gilani
- Department of Civil Engineering, National Institute of Technology, Srinagar, J&K, 190006, India.
| | - Mohammad Shafi Mir
- Transportation & Planning Section, Department of Civil Engineering, National Institute of Technology, Srinagar, J&K, 190006, India
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Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11136030] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
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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Liu Y, Goudreau S, Oiamo T, Rainham D, Hatzopoulou M, Chen H, Davies H, Tremblay M, Johnson J, Bockstael A, Leroux T, Smargiassi A. Comparison of land use regression and random forests models on estimating noise levels in five Canadian cities. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 256:113367. [PMID: 31662255 DOI: 10.1016/j.envpol.2019.113367] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 10/07/2019] [Accepted: 10/08/2019] [Indexed: 05/22/2023]
Abstract
Chronic exposure to environment noise is associated with sleep disturbance and cardiovascular diseases. Assessment of population exposed to environmental noise is limited by a lack of routine noise sampling and is critical for controlling exposure and mitigating adverse health effects. Land use regression (LUR) model is newly applied in estimating environmental exposures to noise. Machine-learning approaches offer opportunities to improve the noise estimations from LUR model. In this study, we employed random forests (RF) model to estimate environmental noise levels in five Canadian cities and compared noise estimations between RF and LUR models. A total of 729 measurements and 33 built environment-related variables were used to estimate spatial variation in environmental noise at the global (multi-city) and local (individual city) scales. Leave one out cross-validation suggested that noise estimates derived from the RF global model explained a greater proportion of variation (R2: RF = 0.58, LUR = 0.47) with lower root mean squared errors (RF = 4.44 dB(A), LUR = 4.99 dB(A)). The cross-validation also indicated the RF models had better general performance than the LUR models at the city scale. By applying the global models to estimate noise levels at the postal code level, we found noise levels were higher in Montreal and Longueuil than in other major Canadian cities.
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Affiliation(s)
- Ying Liu
- Canadian Urban Environmental Health Research Consortium, Canada; Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, QC H3C 3J7, Canada
| | - Sophie Goudreau
- Canadian Urban Environmental Health Research Consortium, Canada; Montreal Regional Department of Public Health, Montreal, QC H2L 1M3, Canada
| | - Tor Oiamo
- Canadian Urban Environmental Health Research Consortium, Canada; Department of Geography and Environmental Studies, Ryerson University, Toronto, ON M5B 2K3, Canada
| | - Daniel Rainham
- Canadian Urban Environmental Health Research Consortium, Canada; Department of Earth and Environmental Sciences, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Marianne Hatzopoulou
- Canadian Urban Environmental Health Research Consortium, Canada; Department of Civil Engineering, University of Toronto, Toronto, ON M5S 1A4, Canada
| | - Hong Chen
- Canadian Urban Environmental Health Research Consortium, Canada; Population Studies Division, Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada; Public Health Ontario, Toronto, ON M5G 1V2, Canada; Institute for Clinical Evaluative Sciences, Toronto, ON M4N 3M5, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
| | - Hugh Davies
- Canadian Urban Environmental Health Research Consortium, Canada; School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Mathieu Tremblay
- Department of Public Health of Montérégie, Longueuil, QC J4K 2M3, Canada
| | - James Johnson
- Canadian Urban Environmental Health Research Consortium, Canada; Public Health Ontario, Toronto, ON M5G 1V2, Canada
| | - Annelies Bockstael
- School of Speech-Language Pathology and Audiology, University of Montreal, QC H3N 1X7, Canada
| | - Tony Leroux
- National Institute of Public Health of Quebec, Montreal, QC H2P 1E2, Canada
| | - Audrey Smargiassi
- Canadian Urban Environmental Health Research Consortium, Canada; Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, QC H3C 3J7, Canada; National Institute of Public Health of Quebec, Montreal, QC H2P 1E2, Canada.
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Liu L, Lei Y. An accurate ecological footprint analysis and prediction for Beijing based on SVM model. ECOL INFORM 2018. [DOI: 10.1016/j.ecoinf.2018.01.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Singh D, Nigam SP, Agrawal VP, Kumar M. Vehicular traffic noise prediction using soft computing approach. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2016; 183:59-66. [PMID: 27576153 DOI: 10.1016/j.jenvman.2016.08.053] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2016] [Revised: 08/11/2016] [Accepted: 08/21/2016] [Indexed: 06/06/2023]
Abstract
A new approach for the development of vehicular traffic noise prediction models is presented. Four different soft computing methods, namely, Generalized Linear Model, Decision Trees, Random Forests and Neural Networks, have been used to develop models to predict the hourly equivalent continuous sound pressure level, Leq, at different locations in the Patiala city in India. The input variables include the traffic volume per hour, percentage of heavy vehicles and average speed of vehicles. The performance of the four models is compared on the basis of performance criteria of coefficient of determination, mean square error and accuracy. 10-fold cross validation is done to check the stability of the Random Forest model, which gave the best results. A t-test is performed to check the fit of the model with the field data.
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Affiliation(s)
- Daljeet Singh
- Department of Mechanical Engineering, Thapar University, Patiala, 147004, Punjab, India.
| | - S P Nigam
- Department of Mechanical Engineering, Thapar University, Patiala, 147004, Punjab, India
| | - V P Agrawal
- Department of Mechanical Engineering, Thapar University, Patiala, 147004, Punjab, India
| | - Maneek Kumar
- Department of Civil Engineering, Thapar University, Patiala, 147004, Punjab, India
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Erkaya S, Geymen A, Bostancı B. Modelling and evaluation of light railway system's noise using neural predictors. JOURNAL OF ENVIRONMENTAL HEALTH SCIENCE & ENGINEERING 2015; 13:20. [PMID: 25806111 PMCID: PMC4371722 DOI: 10.1186/s40201-015-0173-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2014] [Accepted: 03/03/2015] [Indexed: 06/04/2023]
Abstract
BACKGROUND Noise is defined as a sound or series of sounds that are considered to be invasive, irritating, objectionable and disruptive to the quality of daily life. Noise is one of the environmental pollutants, and in cities it is usually originated from road traffic, railway traffic, airports, industry etc. The tram is generally considered as environmentally friendly, namely non-polluting and silent. However complaints from residents living along the tramway lines prove that it may sometimes cause annoyance. In this study, a Global Pointing System (GPS) receiver for determining the sampling locations and a frequency based noise measurement system for collecting the noise data are used to analyse the noise level in the city centre. Both environmental (background) and tram noises are measured. RESULTS Three types of neural networks are used to predict the noises of the tram and environment. The results of three approaches indicate that the proposed neural network with Radial Basis Function (RBF) has superior performance to predict the noises of the tram and environment. CONCLUSIONS For making a decision about transportation planning, this network model can help urban planners for evaluating and/or isolating the tram noise in terms of human health.
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Affiliation(s)
- Selçuk Erkaya
- />Engineering Faculty, Mechatronics Engineering Department, Erciyes University, Kayseri, 38039 Turkey
| | - Abdurrahman Geymen
- />Engineering Faculty, Geomatics Engineering Department, Erciyes University, Kayseri, 38039 Turkey
| | - Bülent Bostancı
- />Engineering Faculty, Geomatics Engineering Department, Erciyes University, Kayseri, 38039 Turkey
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Givargis S, Karimi H. A basic neural traffic noise prediction model for Tehran's roads. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2010; 91:2529-2534. [PMID: 20678858 DOI: 10.1016/j.jenvman.2010.07.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2009] [Revised: 06/10/2010] [Accepted: 07/06/2010] [Indexed: 05/29/2023]
Abstract
We present an artificial neural network model to predict hourly A-weighted equivalent sound pressure levels (L(Aeq,1h)) for roads in Tehran at distances less than 4 m from the nearside carriageway edge. Our model uses the UK Calculation of Road Traffic Noise (CORTN) approach. Data were obtained from 50 sampling locations near five roads in Tehran at nearside carriageway edge distances of less than 4 m. The data were randomly assigned to training, testing, and holdout subsets. Model training was carried out using the training and testing subsets and comprised 60% and 20% of the data, respectively. Model validation was performed using the remaining 20% of data as a holdout subset. We examine the overall model efficiency using non-parametric tests, such as the Wilcoxon matched-pairs signed-rank test for the training step and the Kolmogorov-Smirnov test for two independent samples for the validation step. Our results indicate that a neural network approach can be applied for traffic noise prediction in Tehran in a statistically sound manner. The Wilcoxon matched-pairs signed-ranks test detects no significant difference between the absolute testing set errors of the developed neural network and a calibrated version of the CORTN model.
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Affiliation(s)
- Sh Givargis
- HoushAfzar Research Institute, No. 56, Iranshahr St. Tehran 15816-15434, Iran.
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Genaro N, Torija A, Ramos-Ridao A, Requena I, Ruiz DP, Zamorano M. A neural network based model for urban noise prediction. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2010; 128:1738-1746. [PMID: 20968347 DOI: 10.1121/1.3473692] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Noise is a global problem. In 1972 the World Health Organization (WHO) classified noise as a pollutant. Since then, most industrialized countries have enacted laws and local regulations to prevent and reduce acoustic environmental pollution. A further aim is to alert people to the dangers of this type of pollution. In this context, urban planners need to have tools that allow them to evaluate the degree of acoustic pollution. Scientists in many countries have modeled urban noise, using a wide range of approaches, but their results have not been as good as expected. This paper describes a model developed for the prediction of environmental urban noise using Soft Computing techniques, namely Artificial Neural Networks (ANN). The model is based on the analysis of variables regarded as influential by experts in the field and was applied to data collected on different types of streets. The results were compared to those obtained with other models. The study found that the ANN system was able to predict urban noise with greater accuracy, and thus, was an improvement over those models. The principal component analysis (PCA) was also used to try to simplify the model. Although there was a slight decline in the accuracy of the results, the values obtained were also quite acceptable.
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Affiliation(s)
- N Genaro
- Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain.
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Chang IC, Hsiao TY. Short-term model of the production of construction aggregates in Taiwan based on artificial neural networks. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2004; 11:84-90. [PMID: 15108855 DOI: 10.1007/bf02979707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
BACKGROUND Taiwan's geography and limited stock of sandstone have caused sandstone resources to gradually decline to the point of exhaustion after long-term excavation. Moreover, the Taiwanese government has continuously increased the amount of land area near rivers that cannot be excavated to facilitate riverbed remediation and promote conservation of water resources. Accordingly, predicting and managing the annual production of construction aggregates in future construction projects, and dealing appropriately with some thorny problems, for instance, demand that excess supply, excessive excavation, unregulated excavation, and the consequent environmental damage, will significantly affect the efficient use of natural resources in a manner that accords with the national policy of Sustainable Development (SD). METHODS . This study establishes an empirical model for forecasting the annual production of future construction aggregates using Artificial Neural Networks (ANN), based on 15 relevant socio-economic indicators, such as indicator of annual consumption of cement. A sensitivity analysis is then performed on these indicators. RESULTS AND DISCUSSION This work applies ANN to estimate the annual production of construction aggregates; the estimates, the verification of the model and the sensitivity analysis are all acceptable. Furthermore, sensitivity analysis results indicate that the annual consumption of cement is the indicator that most strongly influences the production of construction aggregates, as well as whether construction waste can be recycled and steel structures can be used in buildings, helping to reduce the future production of construction aggregates in Taiwan. CONCLUSIONS The elaborate prediction methodology presented in this study avoids some of the weaknesses or limitations of conventional linear statistics, linear programming or system dynamics. Additionally, the results not only provide a short-term prediction of the production of construction aggregates in Taiwan, but also provide a viable and flexible means of verifying quality certification of the production data of construction aggregates in the future by incorporating those relevant socio-economic indicators. RECOMMENDATIONS AND OUTLOOK The continuity and quality of the database of relevant indicators used in this study should be closely scrutinized in order to ensure the SD means of exploiting resources.
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Affiliation(s)
- I-Cheng Chang
- Department of Environmental Engineering, Lan Yang Institute of Technology, No. 79, Fushing Rd., Toucheng Jen, Ilan, Taiwan 261.
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Picaut J, Hardy J, Simon L. Sound propagation in urban areas: a periodic disposition of buildings. PHYSICAL REVIEW. E, STATISTICAL PHYSICS, PLASMAS, FLUIDS, AND RELATED INTERDISCIPLINARY TOPICS 1999; 60:4851-9. [PMID: 11970350 DOI: 10.1103/physreve.60.4851] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/1998] [Revised: 05/21/1999] [Indexed: 04/18/2023]
Abstract
A numerical simulation of background noise propagation is performed for a network of hexagonal buildings. The obtained results suggest that the prediction of background noise in urban spaces is possible by means of a modified diffusion equation using two parameters: the diffusion coefficient that expresses the spreading out of noise resulting from diffuse scattering and multiple reflections by buildings, and an attenuation term accounting for the wall absorption, atmospheric attenuation, and absorption by the open top. The dependence of the diffusion coefficient with geometrical shapes and the diffusive nature of the buildings are investigated in the case of a periodic disposition of hexagonal buildings.
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Affiliation(s)
- J Picaut
- Laboratoire Central des Ponts et Chaussées, Centre de Nantes, Route de Bouaye, Boîte Postale 4129, 44341 Bouguenais Cedex, France
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