Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction

Ellysia Jumin, Nuratiah Zaini, Ali Najah Ahmed, Samsuri Abdullah, Marzuki Ismail, Mohsen Sherif, Ahmed Sefelnasr, Ahmed El-Shafie

Research output: Contribution to journalArticlepeer-review

62 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)713-725
Number of pages13
JournalEngineering Applications of Computational Fluid Mechanics
Issue number1
Publication statusPublished - Jan 1 2020


  • Boosted Decision Tree Regression
  • Ozone concentration prediction
  • Pearson Correlation
  • linear regression
  • machine learning algorithm
  • neural network
  • ozone precursors

ASJC Scopus subject areas

  • General Computer Science
  • Modelling and Simulation


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