Article: Studies of air quality predictors based on neural networks Journal: International Journal of Environment and Pollution (IJEP) 2003 Vol.19 No.5 pp.442 - 453 Abstract: In recent years, urban air pollution has emerged as an acute problem because of its negative effect on health and living conditions. Regional air quality problems, in general, are linked to violations of specified air quality standards. The current study aims to find neural network based air quality predictors, which can work with a limited number of datasets and are robust enough to handle data with noise and errors. A number of available variations of neural network models, such as the Recurrent Network Model (RNM), the Change Point Detection Model with RNM (CPDM), the Sequential Network Construction Model (SNCM), the Self Organising Feature Model (SOFM), and the Moving Window Model (MWM), were implemented using MATLAB software for predicting air quality. Developed models were run to simulate and forecast based on the annual average data for 15 years from 1985 to 1999 for seven parameters, viz. VOC, NOx, CO, SO2, PM10, PM2.5 and NH3 for one county of California, USA. The models were fitted with first nine years of data to predict data for remaining six years. The models, in general, could predict air quality patterns with modest accuracy. However, the SOFM model performed extremely well in comparison with the other models for predicting long-term (annual) data. Inderscience Publishers - linking academia, business and industry through research

Title: Studies of air quality predictors based on neural networks

Authors: Sameer Sharma, S.V. Barai, A.K. Dikshit

Addresses: Department of Civil Engineering, Indian Institute of Technology, Kharagpur 721 302, India. ' Department of Civil Engineering, Indian Institute of Technology, Kharagpur 721 302, India. ' Centre for Environmental Science and Engineering, Indian Institute of Technology, Bombay Powai, Mumbai 400 076, India

Abstract: In recent years, urban air pollution has emerged as an acute problem because of its negative effect on health and living conditions. Regional air quality problems, in general, are linked to violations of specified air quality standards. The current study aims to find neural network based air quality predictors, which can work with a limited number of datasets and are robust enough to handle data with noise and errors. A number of available variations of neural network models, such as the Recurrent Network Model (RNM), the Change Point Detection Model with RNM (CPDM), the Sequential Network Construction Model (SNCM), the Self Organising Feature Model (SOFM), and the Moving Window Model (MWM), were implemented using MATLAB software for predicting air quality. Developed models were run to simulate and forecast based on the annual average data for 15 years from 1985 to 1999 for seven parameters, viz. VOC, NO_x, CO, SO₂, PM₁₀, PM_2.5 and NH₃ for one county of California, USA. The models were fitted with first nine years of data to predict data for remaining six years. The models, in general, could predict air quality patterns with modest accuracy. However, the SOFM model performed extremely well in comparison with the other models for predicting long-term (annual) data.

Keywords: air quality; change point detection; recurrent neural networks; self-organising feature maps; urban air pollution.

DOI: 10.1504/IJEP.2003.004327

International Journal of Environment and Pollution, 2003 Vol.19 No.5, pp.442 - 453

Published online: 10 May 2004 *

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Title: Studies of air quality predictors based on neural networks

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