Prediction of Ambient PM10 and Toxic Metals Using Artificial Neural Networks

In this study, an artificial neural network is employed to predict the concentration of ambient respirable particu-late matter (PM 10 ) and toxic metals observed in the city of Jaipur, India. A feed-forward network with a back-propagation learning algorithm is used to train the neural network the be...

Full description

Saved in:
Bibliographic Details
Published in:Journal of the Air & Waste Management Association (1995) 2002-07, Vol.52 (7), p.805-810
Main Authors: Chelani, Asha B., Gajghate, D.G., Hasan, M.Z.
Format: Article
Language:English
Subjects:
Air Pollutants - analysis
Air Pollution - prevention & control
Applied sciences
Atmospheric pollution
Biodegradation, Environmental
Cities
Environment
Exact sciences and technology
Forecasting
Metals, Heavy - analysis
Meteorological Concepts
Neural Networks (Computer)
Particle Size
Pollutants physicochemistry study: properties, effects, reactions, transport and distribution
Pollution
Reference Values
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this study, an artificial neural network is employed to predict the concentration of ambient respirable particu-late matter (PM 10 ) and toxic metals observed in the city of Jaipur, India. A feed-forward network with a back-propagation learning algorithm is used to train the neural network the behavior of the data patterns. The meteorological variables of wind speed, wind direction, relative humidity, temperature, and time are taken as input to the network. The results indicate that the network is able to predict concentrations of PM 10 and toxic metals quite accurately.
ISSN:1096-2247
2162-2906
DOI:10.1080/10473289.2002.10470827