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.
|Title of host publication||2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|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
|Name||Proceedings of the International Joint Conference on Neural Networks|
|Conference||2020 International Joint Conference on Neural Networks, IJCNN 2020|
|Period||19/07/20 → 24/07/20|
Bibliographical noteFunding 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)).
© 2020 IEEE.
- Ozone prediction model
- Robust Regression