Predictive Ability of Improved Neural Network Models to Simulate Pollutant Dispersion. (26th June 2014)

• Record Type: Journal Article
• Authors: Hossain, Khandaker M. A.
• Other Names: Zanis Prodromos Academic Editor.
• Is Part Of: International journal of atmospheric sciences. Volume 2014(2014)
• Publication Date: 2014-06-26
• Subjects: Atmospheric physics -- Periodicals; Atmospheric chemistry -- Periodicals; Atmospheric chemistry; Atmospheric physics; Periodicals; Electronic journals; 551.5
• DOI: 10.1155/2014/141923
• Languages: English
• ISSNs: 2314-4122

Abstract

Abstract : This paper describes the ability of artificial neural network (ANN) models to simulate the pollutant dispersion characteristics in varying urban atmospheres at different regions. ANN models are developed based on twelve meteorological (including rainfall/precipitation) and six traffic parameters/variables that have significant influence on emission/pollutant dispersion. The models are trained to predict concentration of carbon monoxide and particulate matters in urban atmospheres using field meteorological and traffic data. Training, validation, and testing of ANN models are conducted using data from the Dhaka city of Bangladesh. The models are used to simulate concentration of pollutants as well as the effect of rainfall on emission dispersion throughout the year and inversion condition during the night. The predicting ability and robustness of the models are then determined by using data of the coastal cities of Chittagong and Dhaka. ANN models based on both meteorological and traffic variables exhibit the best performance and are capable of resolving patterns of pollutant dispersion to the atmosphere for different cities.