TY - JOUR
T1 - Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction
AU - Jumin, Ellysia
AU - Zaini, Nuratiah
AU - Ahmed, Ali Najah
AU - Abdullah, Samsuri
AU - Ismail, Marzuki
AU - Sherif, Mohsen
AU - Sefelnasr, Ahmed
AU - El-Shafie, Ahmed
N1 - Funding Information:
This work was supported by Ministry of Higher Education Malaysia providing a fundamental research grant: [Grant Number FRGS/1/2018/TK10/UNITEN/03/2]. The authors would like to acknowledge the Ministry of Higher Education Malaysia providing a fundamental research grant scheme No: FRGS/1/2018/TK10/UNITEN/03/2. In addition, the authors would like to thank the Malaysian Meteorological Department (MetMalaysia) for providing relevant data.
Funding Information:
The authors would like to acknowledge the Ministry of Higher Education Malaysia providing a fundamental research grant scheme No: FRGS/1/2018/TK10/UNITEN/03/2. In addition, the authors would like to thank the Malaysian Meteorological Department (MetMalaysia) for providing relevant data.
Publisher Copyright:
© 2020, © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - High level of tropospheric ozone concentration, exceeding allowable level has been frequently reported in Malaysia. This study proposes accurate model based on Machine Learning algorithms to predict Tropospheric ozone concentration in major cities located in Kuala Lumpur and Selangor, Malaysia. The proposed models were developed using three-year of historical data for different parameters as input to predict 24-hour and 12-hour of tropospheric ozone concentration. Different Machine Learning algorithms have been investigated, viz. Linear Regression, Neural Network and Boosted Decision Tree. The results revealed that wind speed, humidity, Nitrogen Oxide, Carbon Monoxide and Nitrogen Dioxide have significant influence on ozone formation. Boosted Decision Tree outperformed Linear regression and Neural Network algorithms for all stations. The performance of the proposed model improved by using 12-hours dataset instead of the 24-hour where R2 values were equal to 0.91, 0.88 and 0.87 for the three investigated stations. To assess the uncertainties of the Boosted Decision Tree model, 95% prediction uncertainties (95PPU) d-factors were introduced.95PPU showed about 94.4, 93.4, 96.7% and the d-factors were 0.001015, 0.001016 and 0.001124 which relate to S1, S2 and S3, respectively. The obtained results provide a reliable prediction model to mimic actual ozone concentration in different locations in Malaysia.
AB - High level of tropospheric ozone concentration, exceeding allowable level has been frequently reported in Malaysia. This study proposes accurate model based on Machine Learning algorithms to predict Tropospheric ozone concentration in major cities located in Kuala Lumpur and Selangor, Malaysia. The proposed models were developed using three-year of historical data for different parameters as input to predict 24-hour and 12-hour of tropospheric ozone concentration. Different Machine Learning algorithms have been investigated, viz. Linear Regression, Neural Network and Boosted Decision Tree. The results revealed that wind speed, humidity, Nitrogen Oxide, Carbon Monoxide and Nitrogen Dioxide have significant influence on ozone formation. Boosted Decision Tree outperformed Linear regression and Neural Network algorithms for all stations. The performance of the proposed model improved by using 12-hours dataset instead of the 24-hour where R2 values were equal to 0.91, 0.88 and 0.87 for the three investigated stations. To assess the uncertainties of the Boosted Decision Tree model, 95% prediction uncertainties (95PPU) d-factors were introduced.95PPU showed about 94.4, 93.4, 96.7% and the d-factors were 0.001015, 0.001016 and 0.001124 which relate to S1, S2 and S3, respectively. The obtained results provide a reliable prediction model to mimic actual ozone concentration in different locations in Malaysia.
KW - Boosted Decision Tree Regression
KW - Ozone concentration prediction
KW - Pearson Correlation
KW - linear regression
KW - machine learning algorithm
KW - neural network
KW - ozone precursors
UR - http://www.scopus.com/inward/record.url?scp=85084835158&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084835158&partnerID=8YFLogxK
U2 - 10.1080/19942060.2020.1758792
DO - 10.1080/19942060.2020.1758792
M3 - Article
AN - SCOPUS:85084835158
SN - 1994-2060
VL - 14
SP - 713
EP - 725
JO - Engineering Applications of Computational Fluid Mechanics
JF - Engineering Applications of Computational Fluid Mechanics
IS - 1
ER -