Vehicular traffic noise prediction using soft computing approach.
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 soun...
| Publicado en: | Journal of Environmental Management Vol. 183; pp. 59 - 67 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Academic Press Inc.
Dec2016 Part 1
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=118342688&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 118342688 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03014797 EMJ jtl: Journal of Environmental Management issn: 03014797 maglogo: N pubinfo: dt: Dec2016 Part 1 vid: 183 pid: 735 pub: Academic Press Inc. artinfo: ui: 118342688 10.1016/j.jenvman.2016.08.053 ppf: 59 ppct: 8 formats: tig: atl: Vehicular traffic noise prediction using soft computing approach. aug: au: Singh, Daljeet Nigam, S.P. Agrawal, V.P. Kumar, Maneek affil: Department of Mechanical Engineering, Thapar University, Patiala, 147004, Punjab, India Department of Civil Engineering, Thapar University, Patiala, 147004, Punjab, India su: Traffic noise Soft computing Decision trees Random forest algorithms Artificial neural networks sug: subj: Traffic noise Soft computing Decision trees Random forest algorithms Artificial neural networks keyword: Modelling Soft computing methods Vehicular traffic noise Modelling Soft computing methods Vehicular traffic noise ab: 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, L eq , 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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