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Comparative Analysis of Regression based and Supervised Learning Algorithms for Predicting Traffic Noise Levels in Indian Scenario

by Prashant Ruwali, Vikas Tripathi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 15
Year of Publication: 2013
Authors: Prashant Ruwali, Vikas Tripathi
10.5120/12965-0240

Prashant Ruwali, Vikas Tripathi . Comparative Analysis of Regression based and Supervised Learning Algorithms for Predicting Traffic Noise Levels in Indian Scenario. International Journal of Computer Applications. 74, 15 ( July 2013), 45-50. DOI=10.5120/12965-0240

@article{ 10.5120/12965-0240,
author = { Prashant Ruwali, Vikas Tripathi },
title = { Comparative Analysis of Regression based and Supervised Learning Algorithms for Predicting Traffic Noise Levels in Indian Scenario },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 15 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 45-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume74/number15/12965-0240/ },
doi = { 10.5120/12965-0240 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:42:25.411229+05:30
%A Prashant Ruwali
%A Vikas Tripathi
%T Comparative Analysis of Regression based and Supervised Learning Algorithms for Predicting Traffic Noise Levels in Indian Scenario
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 15
%P 45-50
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Road traffic noise has remained one of the greatest concerns during the past few decades. it has found to be the major sources of pollution in the metropolitan city areas [7]. With the increase in urbanization and motorization the number of vehicles has increased which further increased this problem by manifolds. [4] Thus, in view of the above stated problem our aim is perform prediction of noise levels using certain available regression based and supervised learning algorithms. Modelling and prediction of traffic noise by using generally used prediction algorithms is a very complicated and non linear process, due to high involvement of several factors over which noise level depends. [3]. However, after analysis we have been able to found appropriate results with a certain levels of accuracy.

References
  1. K. Kumar, V. K. Katiyar, M. Parida, and K. Rawat, "Mathematical modelling of road traffic noise prediction"?, Int. J. of Appl. Math and Mech. pp: 21-28, 2011.
  2. K. Rawat, V. K. Katiyar, Pratibha, "Mathematical Modeling of Environmental Noise Impact"
  3. Kranti KUMAR, Manoranjan PARIDA and Vinod K. KATIYAR, "Road Traffic Noise Prediction with Neural Networks-A Review", An International Journal of Optimization and Control: Theories & Applications, Vol. 2, No. 1, pp. 29-37 (2012)
  4. V. Tripathi, A. Mittal and P. Ruwali "Efficient Road Traffic Noise Model for Generating Noise Levels in Indian Scenario" International Journal of Computer Applications (0975 – 8887) Volume 38– No. 4, January 2012
  5. Daniel, G. N, ?Cause and Effects of Noise Pollution. Interdisciplinary Minor in Global Sustainability?, 1998.
  6. Fyhri, A. and Klæboe. R. , ?Road traffic noise, sensitivity, annoyance and self-reported health —A structural equation model exercise? Environment International, v. 35, pp: 91–97, 2009.
  7. R. Golmohammadi, M. Abbaspour, P. Nassiri, H. Mahjub "A COMPACT MODEL FOR PREDICTING ROAD TRAFFIC NOISE", Iran. J. Environ. Health. Sci. Eng. , 2009, Vol. 6, No. 3, pp. 181-186
  8. Prof. Thomas B. Fomby Department of Economics Southern Methodist University "K-Nearest Neighbours Algorithm: Prediction and Classification"
  9. Alfredo Calixto, Fabiano B. Diniz, Paulo H. T. Zannin "The statistical modeling of road traffic noise in an urban setting Cities",Vol. 20, No. 1, p. 23–29, 2003
  10. S V Barai, A K Dikshit, Sameer Sharma Neural "Network Models for Air Quality Prediction: A Comparative Study"
  11. F. Sarmadian, R. Taghizadeh Mehrjardi and A. Akbarzadeh, "Modeling of some soil properties using artificial neural network and multivariate regression in Gorgan province, north of Iran", Australian J. of Basic and Applied Sci. , Vol. 3, No. 1, pp 323-329, 2009.
  12. F. Karaca and B. Ozkaya, "NN-LEAP: A neural network-based model for controlling leachate flow-rate ina municipal solid waste landfill site", Environ. Modell. Software. , Vol. 21, pp 1190-1197, 2006.
  13. F. Sarmadian, R. Taghizadeh Mehrjardi and A. Akbarzadeh, "Modeling of some soil properties using artificial neural network and multivariate regression in Gorgan province, north of Iran", Australian J. of Basic and Applied Sci. , Vol. 3, No. 1, pp 323-329, 2009.
  14. R. K. Mishra, M. Parida, S. Rangnekar, ?Evaluation and analysis of traffic noise along bus rapid transit system corridor?, Int. J. Environ. Sci. Tech. , pp:737-750, Autumn 2010.
  15. Lars Kai Hansen and Peter Salamon "Neural networks Ensembles" IEEE Tranasactions on pattern analysis and machine intelligence. Vol. 12, NO. 10, October 1990.
Index Terms

Computer Science
Information Sciences

Keywords

Road traffic model (RTM) Artificial neural networks K-nearest neighbour Multi linear regression Polynomial regression Road traffic Noise

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