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Improving Air Pollution Prediction Modelling Using Wrapper Feature Selection

Author

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  • Ahmad Zia Ul-Saufie

    (Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam 40450, Selangor, Malaysia)

  • Nurul Haziqah Hamzan

    (Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam 40450, Selangor, Malaysia)

  • Zulaika Zahari

    (Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam 40450, Selangor, Malaysia)

  • Wan Nur Shaziayani

    (Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam 40450, Selangor, Malaysia)

  • Norazian Mohamad Noor

    (Faculty of Civil Engineering Technology, Universiti Malaysia Perlis, Kompleks Pengajian Jejawi 3, Arau 02600, Perlis, Malaysia)

  • Mohd Remy Rozainy Mohd Arif Zainol

    (School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia)

  • Andrei Victor Sandu

    (Faculty of Material Science and Engineering, Gheorghe Asachi Technical University of Iasi, 61 D. Mangeron Blvd., 700050 Iasi, Romania
    Romanian Inventors Forum, St. P. Movila 3, 700089 Iasi, Romania
    National Institute for Research and Development in Environmental Protection INCDPM, Splaiul Independentei 294, 060031 Bucharest, Romania)

  • Gyorgy Deak

    (National Institute for Research and Development in Environmental Protection INCDPM, Splaiul Independentei 294, 060031 Bucharest, Romania)

  • Petrica Vizureanu

    (Faculty of Material Science and Engineering, Gheorghe Asachi Technical University of Iasi, 61 D. Mangeron Blvd., 700050 Iasi, Romania
    Technical Sciences Academy of Romania, Dacia Blvd 26, 030167 Bucharest, Romania)

Abstract

Feature selection is considered as one of the essential steps in data pre-processing. However, all of the previous studies on predicting PM 10 concentration in Malaysia have been limited to statistical method feature selection, and none of these studies used machine-learning approaches. Therefore, the objective of this research is to investigate the influence variables of the PM 10 prediction model by using wrapper feature selection to compare the prediction model performance of different wrapper feature selection and to predict the concentration of PM 10 for the next day. This research uses 10 years of daily data on pollutant concentrations from two stations (Klang and Shah Alam) obtained from the Department of Environment Malaysia (DOE) from 2009 until 2018. Six wrapper methods (forward selection, backward elimination, stepwise, brute-force, weight-guided and genetic algorithm evolution and the predictive analytics multiple linear regression (MLR) and artificial neural network (ANN)) were implemented in this study. This study found that brute-force is the dominant wrapper method in most of the best models in selecting important features for MLR. Moreover, compared to MLR, ANN provides more advantages regarding model accuracy and permits feature selection in predicting PM 10 . The overall results revealed that the RMSE value for next day prediction in Klang is 20.728, while the AE value is 15.69. Furthermore, the RMSE value for next day prediction in Shah Alam is 10.004, while the AE value is 7.982. Finally, all of the predicted models in Klang and Shah Alam can be used to predict the PM 10 concentrations. This proposed model can be used as a tool for an early warning system in giving air quality information to local authorities in order to formulate air-quality-improvement strategies.

Suggested Citation

  • Ahmad Zia Ul-Saufie & Nurul Haziqah Hamzan & Zulaika Zahari & Wan Nur Shaziayani & Norazian Mohamad Noor & Mohd Remy Rozainy Mohd Arif Zainol & Andrei Victor Sandu & Gyorgy Deak & Petrica Vizureanu, 2022. "Improving Air Pollution Prediction Modelling Using Wrapper Feature Selection," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11403-:d:912424
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    References listed on IDEAS

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    1. Wu, Binrong & Wang, Lin & Zeng, Yu-Rong, 2022. "Interpretable wind speed prediction with multivariate time series and temporal fusion transformers," Energy, Elsevier, vol. 252(C).
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    1. Chelladurai Aarthi & Varatharaj Jeya Ramya & Przemysław Falkowski-Gilski & Parameshachari Bidare Divakarachari, 2023. "Balanced Spider Monkey Optimization with Bi-LSTM for Sustainable Air Quality Prediction," Sustainability, MDPI, vol. 15(2), pages 1-16, January.

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