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

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-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 languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169262
DOIs
Publication statusPublished - 19 Jul 2020
Event2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2020 International Joint Conference on Neural Networks, IJCNN 2020
Country/TerritoryUnited Kingdom
CityVirtual, Glasgow
Period19/07/2024/07/20

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

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