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Título
Predicting Benzene Concentration Using Machine Learning and Time Series Algorithms
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
Mathematics
Número de la revista
12
Cita Bibliográfica
Menéndez García, L. A., Sánchez Lasheras, F., García Nieto, P. J., Álvarez de Prado, L., & Bernardo Sánchez, A. (2020). Predicting benzene concentration using machine learning and time series algorithms. Mathematics, 8(12), 1-21. https://doi.org/10.3390/MATH8122205
Editorial
MDPI
Fecha
2020
Resumen
[EN] 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 (NO2), nitrogen oxides (NOx), particulate matter (PM10) and toluene (C7H8), 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.
Materia
Palabras clave
Peer review
SI
URI
DOI
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