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Causal Graph Discovery for Explainable Insights on Marine Biotoxin Shellfish Contamination

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Intelligent Data Engineering and Automated Learning – IDEAL 2023 (IDEAL 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14404))

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Abstract

Harmful algal blooms are natural phenomena that cause shellfish contamination due to the rapid accumulation of marine biotoxins. To prevent public health risks, the Portuguese Institute of the Ocean and the Atmosphere (IPMA) regularly monitors toxic phytoplankton in shellfish production areas and temporarily closes shellfish production when biotoxins concentration exceeds safety limits. However, this reactive response does not allow shellfish producers to anticipate toxic events and reduce economic losses. Causality techniques applied to multivariate time series data can identify the variables that most influence marine biotoxin contamination and, based on these causal relationships, can help forecast shellfish contamination, providing a proactive approach to mitigate economic losses. This study used causality discovery algorithms to analyze biotoxin concentration in mussels Mytilus galloprovincialis and environmental data from IPMA and Copernicus Marine Environment Monitoring Service. We concluded that the toxins that cause diarrhetic and paralytic shellfish poisoning had more predictors than the toxins that cause amnesic poisoning. Moreover, maximum atmospheric temperature, DSP toxins-producing phytoplankton and wind intensity showed causal relationships with toxicity in mussels with shorter lags, while chlorophyll a (chl-a), mean sea surface temperature and rainfall showed causal associations over longer periods. Causal relationships were also found between toxins in nearby production areas, indicating a spread of biotoxins contamination. This study proposes a novel approach to infer the relationships between environmental variables to enhance decision-making and public health safety regarding shellfish consumption in Portugal.

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Acknowledgements

This work was supported by national funds through FundaÇão para a Ciência e a Tecnologia (FCT) through projects UIDB/00297/2020 and UIDP/00297/2020 (NOVA Math), UIDB/00667/2020 and UIDP/00667/2020 (UNIDEMI), UIDB/50008/2020 (IT), UIDB/50021/2020 (INESC-ID), and also the project MATISSE (DSAIPA/DS/0026/2019), and CEECINST/00042/2021, PTDC/CCI-BIO/4180/2020, and PTDC/CTM-REF/2679/2020. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 951970 (OLISSIPO project).

Author information

Authors and Affiliations

  1. Instituto Superior Técnico (IST), 1000-049, Lisbon, Portugal

    Diogo Ribeiro, Filipe Ferraz & Alexandra M. Carvalho

  2. Instituto de Telecomunicações (IT), 1000-049, Lisbon, Portugal

    Diogo Ribeiro, Filipe Ferraz & Alexandra M. Carvalho

  3. INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1000-029, Lisbon, Portugal

    Diogo Ribeiro, Filipe Ferraz & Susana Vinga

  4. Center for Mathematics and Applications (NOVA Math), Department of Mathematics, NOVA SST, 2829-516, Caparica, Portugal

    Marta B. Lopes

  5. UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA SST, 2829-516, Caparica, Portugal

    Marta B. Lopes

  6. IPMA - Instituto Portuguës do Mar e da Atmosfera, 1495-165, Algés, Portugal

    Susana Rodrigues & Pedro Reis Costa

  7. Lisbon Unit for Learning and Intelligent Systems, Lisbon, Portugal

    Alexandra M. Carvalho

Authors
  1. Diogo Ribeiro
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  2. Filipe Ferraz
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  3. Marta B. Lopes
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  4. Susana Rodrigues
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  5. Pedro Reis Costa
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  6. Susana Vinga
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  7. Alexandra M. Carvalho
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Corresponding author

Correspondence to Alexandra M. Carvalho .

Editor information

Editors and Affiliations

  1. University of Évora, Évora, Portugal

    Paulo Quaresma

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Manchester, Manchester, UK

    Hujun Yin

  4. University of Évora, Évora, Portugal

    Teresa Gonçalves

  5. Polytechnic University of Valencia, Valencia, Spain

    Vicente Julian

  6. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

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Ribeiro, D. et al. (2023). Causal Graph Discovery for Explainable Insights on Marine Biotoxin Shellfish Contamination. In: Quaresma, P., Camacho, D., Yin, H., Gonçalves, T., Julian, V., Tallón-Ballesteros, A.J. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2023. IDEAL 2023. Lecture Notes in Computer Science, vol 14404. Springer, Cham. https://doi.org/10.1007/978-3-031-48232-8_44

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  • DOI: https://doi.org/10.1007/978-3-031-48232-8_44

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