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Doctoral Symposium on Information and Communication Technologies - DSICT pp 47–62Cite as

Variational Autoencoders for Assessing Sustainability

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Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 846)

Abstract

Reliable, impartial, and timely data models are urgently needed to inform global sustainability measures. As a response, implicit and explicit fuzzy methods that evaluate qualitative and quantitative variables have been proposed. Meanwhile, decision-makers are often compelled to address immediate over long-term risks. In the era of big data, artificial intelligence, and the internet we need to leverage the power of statistical models to support effective leadership. This article introduces a new technique for evaluating sustainability based on the Sustainability Assessment by Fuzzy Evaluation (SAFE) model: Variational Autoencoder plus graphical analysis (VAE&GA). This approach produces SAFE-like rankings backed by a dual axis chart that groups countries according to their most important unique indicators. VAE&GA is thus a more objective alternative to current fuzzy methods.

Keywords

  • Artificial neural network
  • Sustainability indicators
  • Graphical sustainability ranking

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Authors and Affiliations

  1. Department of Statistics, University of Salamanca, Salamanca, Spain

    José Fernando Romero-Cañizares & Purificación Vicente-Galindo

Authors
  1. José Fernando Romero-Cañizares
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  2. Purificación Vicente-Galindo
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Corresponding author

Correspondence to José Fernando Romero-Cañizares .

Editor information

Editors and Affiliations

  1. CEDIA, Cuenca, Ecuador

    Santiago Berrezueta

  2. CEDIA, Cuenca, Ecuador

    Karina Abad

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Romero-Cañizares, J.F., Vicente-Galindo, P. (2022). Variational Autoencoders for Assessing Sustainability. In: Berrezueta, S., Abad, K. (eds) Doctoral Symposium on Information and Communication Technologies - DSICT. Lecture Notes in Electrical Engineering, vol 846. Springer, Cham. https://doi.org/10.1007/978-3-030-93718-8_5

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  • DOI: https://doi.org/10.1007/978-3-030-93718-8_5

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