Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/5155
Title: Vehicular pollution modeling using artificial neural network technique: A review
Authors: Sharma, N
Chaudhry, K K
Rao, C V Chalapati
Keywords: Air quality management
Vehicular pollution modeling
Artificial neural networks
Multilayer perceptron
Training data
Supervised learning
Issue Date: Sep-2005
Publisher: CSIR
Series/Report no.: G 10 K 11/ 00, G 06 N 3 / 02
Abstract: Air quality models form one of the most important components of an urban air quality management plan. An effective air quality management system must be able to provide the authorities with information on the current and likely future trends, enabling them to make necessary assessments regarding the extent and type of the air pollution control management strategies to be implemented throughout the area. Various statistical modeling techniques (regression, multiple regression and time series analysis) have been used to predict air pollution concentrations in the urban environment. These models calculate pollution concentrations due to observed traffic, meteorological and pollution data after an appropriate relationship has been obtained empirically between these parameters. Recently, statistical modeling tool such as artificial neural network (ANN) is increasingly used as an alternative tool for modeling the pollutants from vehicular traffic particularly in urban areas. In the present paper, a review of the applications of ANN in vehicular pollution modeling under urban condition and basic features of ANN and modeling philosophy, including performance evaluation criteria for ANN based vehicular emission models have been described.
Description: 637-647
URI: http://hdl.handle.net/123456789/5155
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.64(09) [September 2005]

Files in This Item:
File Description SizeFormat 
JSIR 64(9) 637-647.pdf331.13 kBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.