Predicting Benzene Concentration Using Machine Learning and Time Series Algorithms

Luis Alfonso Menéndez García, Fernando Sánchez Lasheras, Paulino José García Nieto, Laura Álvarez de Prado and Antonio Bernardo Sánchez
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Luis Alfonso Menéndez García: Department of Mining Technology, Topography and Structures. Higher and Technical School of Mining Engineering, University of León, Campus de Vegazana s/n, 24071 León, Spain
Fernando Sánchez Lasheras: Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007 Oviedo, Spain
Paulino José García Nieto: Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007 Oviedo, Spain
Laura Álvarez de Prado: Department of Mining Technology, Topography and Structures. Higher and Technical School of Mining Engineering, University of León, Campus de Vegazana s/n, 24071 León, Spain
Antonio Bernardo Sánchez: Department of Mining Technology, Topography and Structures. Higher and Technical School of Mining Engineering, University of León, Campus de Vegazana s/n, 24071 León, Spain

Mathematics, 2020, vol. 8, issue 12, 1-22

Abstract: Benzene is a pollutant which is very harmful to our health, so models are necessary to predict its concentration and relationship with other air pollutants. The data collected by eight stations in Madrid (Spain) over nine years were analyzed using the following regression-based machine learning models: multivariate linear regression (MLR), multivariate adaptive regression splines (MARS), multilayer perceptron neural network (MLP), support vector machines (SVM), autoregressive integrated moving-average (ARIMA) and vector autoregressive moving-average (VARMA) models. Benzene concentration predictions were made from the concentration of four environmental pollutants: nitrogen dioxide (NO 2 ), nitrogen oxides (NO x ), particulate matter (PM 10 ) and toluene (C 7 H 8 ), and the performance measures of the model were studied from the proposed models. In general, regression-based machine learning models are more effective at predicting than time series models.

Keywords: benzene; forecasting; air pollutant; multivariate adaptive regression splines (MLR); multivariate adaptive regression splines (MARS); multilayer perceptron neural network (MLP); support vector machines (SVM); autoregressive integrated moving-average (ARIMA); vector autoregressive moving-average (VARMA) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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