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Hourly Air Quality Index (AQI) Forecasting Using Machine Learning Methods

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15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020) (SOCO 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1268))

Abstract

Air Quality Index (AQI) is an index to inform the daily air quality. AQI is a dimensionless quantity to show the state of air pollution simplifying the information of concentrations in \(\mu g/m^3\). Air quality indexes have been established for each of the five pollutants located in an interesting area to study in as Algeciras (Spain). Hourly data of air pollutants, available during 2010–2015, were analysed for the development of the proposed AQI. This work proposes a two-step forecasting approach to obtain future values, eight hours ahead, of AQI using Machine Learning methods. ANN, SVR and LSTM are capable of modelling non-linear time series and can be trained to accurately generalize when a new database is presented.

Supported by MICINN (Ministerio de Ciencia e Innovación-Spain).

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Acknowledgments

This work is part of the research project RTI2018-098160-B-I00 supported by MICINN (Ministerio de Ciencia e Innovación-Spain). The database has been kindly provided by the Environmental Agency of the Andalusian.

Author information

Authors and Affiliations

  1. Intelligent Modelling of Systems Research Group, Polytechnic School of Engineering (Algeciras), University of Cadiz, Avda. Ramon Puyol s/n, 11202, Algeciras, Spain

    Jose Antonio Moscoso-López, Javier González-Enrique, Juan Jesus Ruiz-Aguilar & Ignacio J. Turias

  2. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006, Burgos, Spain

    Daniel Urda

Authors
  1. Jose Antonio Moscoso-López
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  2. Daniel Urda
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  3. Javier González-Enrique
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  4. Juan Jesus Ruiz-Aguilar
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  5. Ignacio J. Turias
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Corresponding author

Correspondence to Jose Antonio Moscoso-López .

Editor information

Editors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Álvaro Herrero

  2. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Carlos Cambra

  3. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Daniel Urda

  4. Technological Institute of Castilla y León, Burgos, Spain

    Javier Sedano

  5. Department of Industrial Engineering, University of A Coruña, La Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Moscoso-López, J.A., Urda, D., González-Enrique, J., Ruiz-Aguilar, J.J., Turias, I.J. (2021). Hourly Air Quality Index (AQI) Forecasting Using Machine Learning Methods. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_12

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