Predicting Benzene Concentration Using Machine Learning and Time Series Algorithms
[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...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad de León |
| Repositorio: | BULERIA. Repositorio Institucional de la Universidad de León |
| OAI Identifier: | oai:buleria.unileon.es:10612/19019 |
| Acceso en línea: | https://hdl.handle.net/10612/19019 |
| Access Level: | acceso abierto |
| Palabra clave: | Ingeniería de minas 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) 3308.01 Control de la Contaminación Atmosférica 3308.04 Ingeniería de la Contaminación |
| Sumario: | [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. |
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