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Neural Network Modelling for Dissolved Oxygen Effects in Extensive Litopenaeus Vannamei Culture

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Advances in Soft Computing (MICAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10062))

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Abstract

Shrimp aquaculture is an important activity currently practiced worldwide. Dissolved oxygen can be lethal in organisms when low concentrations are present in an extensive cultured pond. According to this, a new computational model for dissolved oxygen assessment using artificial neural networks is proposed. Measurements from environmental parameters related with dissolved oxygen were used, classifying those negative situations that can affect the environmental stability of the ecosystem. As a result, an indicator concerning the good or bad water quality condition is obtained. Finally, comparisons against models reported in literature show the good performance of the proposed model.

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Authors and Affiliations

  1. Centro de Investigación en Computación – Instituto Politécnico Nacional, Av. Juan de Dios Bátiz s/n, Nueva Industrial Vallejo, Gustavo A. Madero, 07738, Mexico, Mexico

    José Juan Carbajal-Hernández & Luis Pastor Sánchez-Fernández

Authors
  1. José Juan Carbajal-Hernández
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  2. Luis Pastor Sánchez-Fernández
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Corresponding author

Correspondence to José Juan Carbajal-Hernández .

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Editors and Affiliations

  1. Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, México DF, Mexico

    Obdulia Pichardo-Lagunas

  2. INFOTEC Aguascalientes, Aguascalientes, Mexico

    Sabino Miranda-Jiménez

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Carbajal-Hernández, J.J., Sánchez-Fernández, L.P. (2017). Neural Network Modelling for Dissolved Oxygen Effects in Extensive Litopenaeus Vannamei Culture. In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds) Advances in Soft Computing. MICAI 2016. Lecture Notes in Computer Science(), vol 10062. Springer, Cham. https://doi.org/10.1007/978-3-319-62428-0_10

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  • DOI: https://doi.org/10.1007/978-3-319-62428-0_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62427-3

  • Online ISBN: 978-3-319-62428-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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