Vehicular pollution modeling using artificial neural network technique: A review

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.