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Time Series Forecasting for Improving Quality of Life and Ecosystem Services in Smart Cities

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Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence (ISAmI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 603))

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Abstract

Quality of life is one of the factors that most influence the mood of citizens. As many studies have shown, one of the ways to increase the perception of quality of life are the actions on the Green Infrastructure of cities. Some studies have resorted to LSTM and ARIMA networks to make environmental predictions, however, as will be shown in this article, the seasonality of these models is a brake on the predictions. In order to perform efficient actions, an application case is presented, which has made use of cutting-edge methodologies thanks to IoT technology, Big Data and Artificial Intelligence to collect environmental data in order to perform time series prediction processes with them using GAM models, which have proven to be the most efficient during the tests carried out. Thanks to this work, it has been possible to obtain information on future environmental scenarios in order to make the best decisions on the influence that urban actions implemented by local authorities will have on citizens.

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Acknowledgements

This work has been partially supported by the Institute for Business Competitiveness of Castilla y León, and the European Regional Development Fund under grant CCTT3/20/SA/0002 (AIR-SCity project).

Author information

Authors and Affiliations

  1. BISITE Research Group, University of Salamanca Edificio Multiusos I+D+i, Calle Espejo 2, 37007, Salamanca, Spain

    Raúl López-Blanco, Juan Herranz Martín, Ricardo S. Alonso & Javier Prieto

  2. AIR Institute—Deep Tech Lab IoT Digital Innovation Hub, Salamanca, Spain

    Ricardo S. Alonso

Authors
  1. Raúl López-Blanco
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  2. Juan Herranz Martín
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  3. Ricardo S. Alonso
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  4. Javier Prieto
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Corresponding author

Correspondence to Raúl López-Blanco .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de València, Valencia, Valencia, Spain

    Vicente Julián

  2. ISEP/GECAD, Porto, Portugal

    João Carneiro

  3. Científico Universidad de Salamanca, AIR Institute Villamayor, Parque, Salamanca, Spain

    Ricardo S. Alonso

  4. Edificio I+D+i (24.2), University of Salamanca, Salamanca, Salamanca, Spain

    Pablo Chamoso

  5. Departamento de Informática, University of Minho, Braga, Portugal

    Paulo Novais

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López-Blanco, R., Martín, J.H., Alonso, R.S., Prieto, J. (2023). Time Series Forecasting for Improving Quality of Life and Ecosystem Services in Smart Cities. In: Julián, V., Carneiro, J., Alonso, R.S., Chamoso, P., Novais, P. (eds) Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence. ISAmI 2022. Lecture Notes in Networks and Systems, vol 603. Springer, Cham. https://doi.org/10.1007/978-3-031-22356-3_8

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