Novel Approach to Predict Ground-Level Ozone Concentration Using S-estimation and MM-Estimimation

Ahmad Zia Ul-Saufie; Dhiya Al-Jumeily; Abir Hussain; Muqhlisah Muhamad; Jamila Musafina; Fawaz Ghali; Thar Baker

doi:10.1109/IJCNN48605.2020.9207203

Novel Approach to Predict Ground-Level Ozone Concentration Using S-estimation and MM-Estimimation

Ahmad Zia Ul-Saufie, Dhiya Al-Jumeily, Abir Hussain, Muqhlisah Muhamad, Jamila Musafina, Fawaz Ghali, Thar Baker

School of Arch, Tech and Eng

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

Abstract

Ground-level ozone concentration is one of the main concerns for air pollution, due to the negative impacts on human health, animals, foliage, climate and the whole ecosystem. The aim of this paper is to reduce the influential outliers by including weightages within robust method to avoid the bias of the model. The influential outliers from x-space (predictors) have been identified using leverage values. Furthermore, Cook's distance and standardized residual have been computed to clarify the influential outliers from both of x-space and y-direction. S-estimation and MM-estimation have been introduced as a new approach for reducing the influential outliers from x-space and both of y-direction and x-space respectively. The comparison between the robust method and the ordinary least square method shows that, the accuracy measures of the robust method have been improved by around 0.94% (D+1), 0.56% (D+2) and 1.85% (D+3) respectively.

Original language	English
Title of host publication	2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728169262
DOIs	https://doi.org/10.1109/IJCNN48605.2020.9207203
Publication status	Published - 19 Jul 2020
Event	2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2020 International Joint Conference on Neural Networks, IJCNN 2020
Country/Territory	United Kingdom
City	Virtual, Glasgow
Period	19/07/20 → 24/07/20

Bibliographical note

Funding Information:
ACKNOWLEDGMENT A special appreciation to the Department of Environmental Malaysia (DoE) for providing the air quality dataset to support this research, with a special thanks to Universiti Teknologi Mara, Malaysia and the Ministry of Higher Education Malaysia for funding this study under the grant number (600-IRMI/FRGS 5/3 (289/2019)).

Publisher Copyright:
© 2020 IEEE.

Keywords

MM-estimation
OLS
Ozone prediction model
Robust Regression
S-estimation

Access to Document

10.1109/IJCNN48605.2020.9207203

Cite this

Ul-Saufie, A. Z., Al-Jumeily, D., Hussain, A., Muhamad, M., Musafina, J., Ghali, F., & Baker, T. (2020). Novel Approach to Predict Ground-Level Ozone Concentration Using S-estimation and MM-Estimimation. In 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings Article 9207203 (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN48605.2020.9207203

Ul-Saufie, Ahmad Zia ; Al-Jumeily, Dhiya ; Hussain, Abir et al. / Novel Approach to Predict Ground-Level Ozone Concentration Using S-estimation and MM-Estimimation. 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. (Proceedings of the International Joint Conference on Neural Networks).

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author = "Ul-Saufie, {Ahmad Zia} and Dhiya Al-Jumeily and Abir Hussain and Muqhlisah Muhamad and Jamila Musafina and Fawaz Ghali and Thar Baker",

note = "Funding Information: ACKNOWLEDGMENT A special appreciation to the Department of Environmental Malaysia (DoE) for providing the air quality dataset to support this research, with a special thanks to Universiti Teknologi Mara, Malaysia and the Ministry of Higher Education Malaysia for funding this study under the grant number (600-IRMI/FRGS 5/3 (289/2019)). Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Joint Conference on Neural Networks, IJCNN 2020 ; Conference date: 19-07-2020 Through 24-07-2020",

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Ul-Saufie, AZ, Al-Jumeily, D, Hussain, A, Muhamad, M, Musafina, J, Ghali, F & Baker, T 2020, Novel Approach to Predict Ground-Level Ozone Concentration Using S-estimation and MM-Estimimation. in 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings., 9207203, Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., 2020 International Joint Conference on Neural Networks, IJCNN 2020, Virtual, Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/IJCNN48605.2020.9207203

Novel Approach to Predict Ground-Level Ozone Concentration Using S-estimation and MM-Estimimation. / Ul-Saufie, Ahmad Zia; Al-Jumeily, Dhiya; Hussain, Abir et al.
2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9207203 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

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T1 - Novel Approach to Predict Ground-Level Ozone Concentration Using S-estimation and MM-Estimimation

AU - Ul-Saufie, Ahmad Zia

AU - Al-Jumeily, Dhiya

AU - Hussain, Abir

AU - Muhamad, Muqhlisah

AU - Musafina, Jamila

AU - Ghali, Fawaz

AU - Baker, Thar

N1 - Funding Information: ACKNOWLEDGMENT A special appreciation to the Department of Environmental Malaysia (DoE) for providing the air quality dataset to support this research, with a special thanks to Universiti Teknologi Mara, Malaysia and the Ministry of Higher Education Malaysia for funding this study under the grant number (600-IRMI/FRGS 5/3 (289/2019)). Publisher Copyright: © 2020 IEEE.

PY - 2020/7/19

Y1 - 2020/7/19

N2 - Ground-level ozone concentration is one of the main concerns for air pollution, due to the negative impacts on human health, animals, foliage, climate and the whole ecosystem. The aim of this paper is to reduce the influential outliers by including weightages within robust method to avoid the bias of the model. The influential outliers from x-space (predictors) have been identified using leverage values. Furthermore, Cook's distance and standardized residual have been computed to clarify the influential outliers from both of x-space and y-direction. S-estimation and MM-estimation have been introduced as a new approach for reducing the influential outliers from x-space and both of y-direction and x-space respectively. The comparison between the robust method and the ordinary least square method shows that, the accuracy measures of the robust method have been improved by around 0.94% (D+1), 0.56% (D+2) and 1.85% (D+3) respectively.

AB - Ground-level ozone concentration is one of the main concerns for air pollution, due to the negative impacts on human health, animals, foliage, climate and the whole ecosystem. The aim of this paper is to reduce the influential outliers by including weightages within robust method to avoid the bias of the model. The influential outliers from x-space (predictors) have been identified using leverage values. Furthermore, Cook's distance and standardized residual have been computed to clarify the influential outliers from both of x-space and y-direction. S-estimation and MM-estimation have been introduced as a new approach for reducing the influential outliers from x-space and both of y-direction and x-space respectively. The comparison between the robust method and the ordinary least square method shows that, the accuracy measures of the robust method have been improved by around 0.94% (D+1), 0.56% (D+2) and 1.85% (D+3) respectively.

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KW - Ozone prediction model

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DO - 10.1109/IJCNN48605.2020.9207203

M3 - Conference contribution with ISSN or ISBN

AN - SCOPUS:85093854968

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 International Joint Conference on Neural Networks, IJCNN 2020

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ER -

Ul-Saufie AZ, Al-Jumeily D, Hussain A, Muhamad M, Musafina J, Ghali F et al. Novel Approach to Predict Ground-Level Ozone Concentration Using S-estimation and MM-Estimimation. In 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9207203. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN48605.2020.9207203

Novel Approach to Predict Ground-Level Ozone Concentration Using S-estimation and MM-Estimimation

Abstract

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Bibliographical note

Keywords

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