Improving Air Pollution Prediction Modelling Using Wrapper Feature Selection

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 and Petrica Vizureanu
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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
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

Sustainability, 2022, vol. 14, issue 18, 1-16

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.

Keywords: hybrid models; air pollution modelling; feature selection; wrapper method; artificial neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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