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Comparative evaluation of gaussian, stochastic and artificial neural network based line source models
Published in
2009
Pages: 1153 - 1165
Abstract
This paper presents a comparative evaluation of deterministic, stochastic and artificial neural network (ANN) based line source models in predicting carbon monoxide (CO) concentrations near an urban roadway/intersection. The observed concentration data of CO for the critical winter period (21 st - 31st December. 1999). at two-air quality control regions- a traffic intersection and an arterial road in the Delhi city, have been compared with model predictions. A range of statistical indicators has been used for model performance evaluation. The results show that ANN based line source models are comparatively more accurate in predicting the CO concentration near urban roadway/intersection than the deterministic and univariate statistical models. Copyright © 2009 by IICAI.
About the journal
JournalProceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009
Open AccessNo
Concepts (19)
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    ARTERIAL ROADS
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    Co concentrations
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    Comparative evaluations
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    CONCENTRATION DATA
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    Gaussians
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    LINE SOURCES
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    Model performance
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    MODEL PERFORMANCE EVALUATIONS
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    Model prediction
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    Statistical indicators
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    Statistical models
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    TRAFFIC INTERSECTIONS
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    UNIVARIATE
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    Air quality
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    Carbon monoxide
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    Neural networks
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    Quality control
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    Stochastic systems
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    Stochastic models