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

• Published in: Proceedings of the International Joint Conference on Neural Networks
• Main Author: Ul-Saufie A.Z.; Al-Jumeily D.; Hussain A.; Muhamad M.; Musafina J.; Ghali F.; Baker T.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2020
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093854968&doi=10.1109%2fIJCNN48605.2020.9207203&partnerID=40&md5=81a178c5f650ec2c92ea66f247a50a31

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. © 2020 IEEE.