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A SO2 Pollution Concentrations Prediction Approach Using Autoencoders

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17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) (SOCO 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 531))

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Abstract

To achieve the prediction of SO2(t + 1) concentration values in the area of the Bay of Algeciras, Autoencoders (AE) and Sparse Autoencoders (SAE) have been applied to analyse air quality in this complex zone. A three-year hourly database of air pollutants, meteorological and vessel data were used to test different prediction scenarios. The data were divided into disjoint quartiles (Q1–Q4). AE models are better performed in the medium values (quartiles Q2 and Q3) and SAE models produce equivalent results in low and high values (Q1 and Q4). The results show that AE layers can be stacked to configure a more complex network with different levels of the sparsity of dimensions, together with a final supervised layer for the prediction of the index of the SO2 level (quartiles Q1–Q4).

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Acknowledgements

This work is part of the research project RTI2018-098160-B-I00 supported by ‘MICINN’ Programa Estatal de I+D+i Orientada a ‘Los Retos de la Sociedad’. Data used in this work have been kindly provided by the Algeciras Bay Port Authority and the Andalusian Regional Government.

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Authors and Affiliations

  1. Higher Technical Engineering School, University of Cádiz, Avd. Ramón Puyol s/n, 11202, Algeciras, (Cádiz), Spain

    M. I. Rodríguez-García, J. González-Enrique, J. J. Ruiz-Aguilar & I. J. Turias

Authors
  1. M. I. Rodríguez-García
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  2. J. González-Enrique
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  3. J. J. Ruiz-Aguilar
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  4. I. J. Turias
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Corresponding author

Correspondence to M. I. Rodríguez-García .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. Mechanical Engineering Department, University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martinez-de-Pison

  4. Inteligencia Artificial, University of Oviedo, A Coruña, La Coruña, Spain

    José Ramón Villar Flecha

  5. Data Science and Big Data Lab, Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso Lora

  6. University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  8. School of engineering, Pablo Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  9. DIGIP, University of Bergamo, Dalmine, Bergamo, Italy

    Giuseppe Psaila

  10. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  11. Department of Computing Science, University of Salamanca, Salamanca, Spain

    Emilio S. Corchado Rodriguez

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Rodríguez-García, M.I., González-Enrique, J., Ruiz-Aguilar, J.J., Turias, I.J. (2023). A SO2 Pollution Concentrations Prediction Approach Using Autoencoders. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_5

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