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A Genetic Algorithm and Neural Network Stacking Ensemble Approach to Improve NO2 Level Estimations

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Advances in Computational Intelligence (IWANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11506))

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Abstract

This work investigates the possible improvements that a stacked ensemble can provide to NO2 estimations in a monitoring network located in the Bay of Algeciras (Spain). In the proposed ensemble, ANNs, linear and nonlinear genetic algorithms models have been used as the individual learners in the first stage. The non-linear GA models produce better results than linear GA models as they are able to detect useful relationships between variables that are ignored in the linear case. The outputs of the individual learners have been employed as the inputs of the ANN models of the second stage. The most accurate of these models produced the final NO2 estimation. The obtained results are promising as this final stage-2 model is able to outperform all the other estimation models considered in this work. This can be explained due to its ability to exploit the advantages offered by each individual model from stage-1 and then find an optimal combination of their outputs in order to increase the global estimation performance. The improvement of these NO2 estimations can be very useful to improve the autonomous capacities for monitoring networks.

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Acknowledgements

This work is part of the coordinated research projects TIN2014-58516-C2-1-R and TIN2014-58516-C2-2-R supported by MICINN (Ministerio de Economía y Competitividad-Spain). Monitoring data have been kindly provided by the Environmental Agency of the Andalusian Government.

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Authors and Affiliations

  1. Department of Computer Science Engineering, Polytechnic School of Engineering, University of Cádiz, Avda. Ramón Puyol, s/n, 11202, Algecira, Cádiz, Spain

    Javier González-Enrique, Steffanie Van Roode, Daniel Urda & Ignacio J. Turias

  2. Department of Industrial and Civil Engineering, Polytechnic School of Engineering, University of Cádiz, Avda. Ramón Puyol, s/n, 11202, Algeciras, Cádiz, Spain

    Juan Jesús Ruiz-Aguilar & José Antonio Moscoso-López

Authors
  1. Javier González-Enrique
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  2. Juan Jesús Ruiz-Aguilar
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  3. José Antonio Moscoso-López
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  4. Steffanie Van Roode
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  5. Daniel Urda
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  6. Ignacio J. Turias
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Corresponding author

Correspondence to Javier González-Enrique .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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González-Enrique, J., Ruiz-Aguilar, J.J., Moscoso-López, J.A., Van Roode, S., Urda, D., Turias, I.J. (2019). A Genetic Algorithm and Neural Network Stacking Ensemble Approach to Improve NO2 Level Estimations. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11506. Springer, Cham. https://doi.org/10.1007/978-3-030-20521-8_70

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  • DOI: https://doi.org/10.1007/978-3-030-20521-8_70

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20520-1

  • Online ISBN: 978-3-030-20521-8

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