Comparing advanced regression methods for the prediction of PM2.5 air pollution

Herbert F. Jelinek; Andrei V. Kelarev; Anthony Kolbe; S Heidenreich; Tracey Oakman

Comparing advanced regression methods for the prediction of PM2.5 air pollution

Herbert F. Jelinek, Andrei V. Kelarev, Anthony Kolbe, S Heidenreich, Tracey Oakman

Research output: Contribution to journal › Article › peer-review

Abstract

In this article, we present new results of experiments comparing the effectiveness of regression methods in their ability to predict the NSW classification of the level of PM2.5 particles in the air. We used an extensive data set for Wagga Wagga obtained from the DustWatch program and the Bureau of Meteorology, Australia over a twelve months period. The best outcomes were obtained using Additive Regression method based on Isotonic Regression, which is a novel iterative method.

Original language	English
Pages (from-to)	65-68
Number of pages	4
Journal	Advances in Computer Science and Engineering
Volume	13
Issue number	1
Publication status	Published - 2014

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title = "Comparing advanced regression methods for the prediction of PM2.5 air pollution",

abstract = "In this article, we present new results of experiments comparing the effectiveness of regression methods in their ability to predict the NSW classification of the level of PM2.5 particles in the air. We used an extensive data set for Wagga Wagga obtained from the DustWatch program and the Bureau of Meteorology, Australia over a twelve months period. The best outcomes were obtained using Additive Regression method based on Isotonic Regression, which is a novel iterative method.",

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AU - Jelinek, Herbert F.

AU - Kelarev, Andrei V.

AU - Kolbe, Anthony

AU - Heidenreich, S

AU - Oakman, Tracey

N1 - Includes bibliographical references.

PY - 2014

Y1 - 2014

N2 - In this article, we present new results of experiments comparing the effectiveness of regression methods in their ability to predict the NSW classification of the level of PM2.5 particles in the air. We used an extensive data set for Wagga Wagga obtained from the DustWatch program and the Bureau of Meteorology, Australia over a twelve months period. The best outcomes were obtained using Additive Regression method based on Isotonic Regression, which is a novel iterative method.

AB - In this article, we present new results of experiments comparing the effectiveness of regression methods in their ability to predict the NSW classification of the level of PM2.5 particles in the air. We used an extensive data set for Wagga Wagga obtained from the DustWatch program and the Bureau of Meteorology, Australia over a twelve months period. The best outcomes were obtained using Additive Regression method based on Isotonic Regression, which is a novel iterative method.

M3 - Article

SN - 0973-6999

VL - 13

SP - 65

EP - 68

JO - Advances in Computer Science and Engineering

JF - Advances in Computer Science and Engineering

IS - 1

ER -

Comparing advanced regression methods for the prediction of PM2.5 air pollution

Abstract

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