alexa Forecasting the Tropospheric Ozone using Artificial Neural Network Modelling Approach: A Case Study of Megacity Madras, India | OMICS International | Abstract
ISSN: 2165-784X

Journal of Civil & Environmental Engineering
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Research Article

Forecasting the Tropospheric Ozone using Artificial Neural Network Modelling Approach: A Case Study of Megacity Madras, India

Anurag Kandya1*, Shiva Nagendra SM2 and Vivek Kumar Tiwari1
1Department of Civil Engineering, Institute of Technology, Nirma University, Ahmedabad, India
2Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India
Corresponding Author : Anurag Kandya
Department of Civil Engineering
Institute of Technology
Nirma University
Ahmedabad, India
E-mail: [email protected]
Received September 12, 2012; Accepted October 26, 2012; Published November 06, 2012
Citation: Kandya A, Shiva Nagendra SM, Tiwari VK (2012) Forecasting the Tropospheric Ozone using Artificial Neural Network Modelling Approach: A Case Study of Megacity Madras, India. J Civil Environ Eng S1:006. doi:10.4172/2165-784X.S1-006
Copyright: © 2012 Kandya A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Ozone, which is a secondary pollutant in the troposphere, is very injurious to human health causing irritation of respiratory system, reducing lung capacity, etc. Adversely affecting the plant growth and deteriorating the materials. Thus it is of prime importance to predict the ozone concentration so that effective mitigation strategies can be adopted. As the formation of the tropospheric ozone is dependent on various meteorological parameters and the concentration of various other air pollutants, it is necessary to consider this dependence aspect in the modelling approach. In this background, the present paper puts forward the study of short-term prediction of tropospheric ozone concentration using Artificial Neural Network (ANN) modelling approach for a busy traffic junction of Madras city, one of the four megacities of India. 8-hourly averaged values of 11 air pollutants concentrations and 6 meteorological parameters were used for the study. The respective data was collected at a busy traffic junction of the city for a period of 19 months i.e. during September 2008–March 2010. 70% of the data was used for training the ANN models while the remaining of 30% data was used for validating them. By changing the neural architecture, 34 ANN models were formulated which were statistically analyzed. Based on the encouraging results (d=0.80, r=0.69, etc.), the paper puts forward the suitability of ANN modelling approach for the short-term prediction of tropospheric ozone concentration.

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