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Assessment and prediction of water quality in shrimp culture using signal processing techniques

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Abstract

In recent years, artificial intelligence methods have proved appropriate for the treatment of environmental problems. This paper presents a novel work for the assessment and prediction of water quality in shrimp aquaculture based on environmental pattern processing. Water quality studies are based on analyzing negative concentrations of compounds in shrimp ponds that inhibit good growth and reproduction of organisms. The physical–chemical variables are classified based on the negative ecological impact using the Gamma (Γ) classifier, which calculates the frequency and deviation of the measurements from a specific level. A fuzzy inference system processes the level classifications using a reasoning process that determines when a specific concentration is good or harmful for the organism and provides a water quality index, which describes the condition of the ecosystem: excellent, good, regular, and poor. An autoregressive model (AR) predicts a section of an environmental signal using historical information, and the set of predicted variables are assessed in order to estimate future water quality conditions in the system. This methodology emerges as a suitable and alternative tool to be used in developing effective water management plans.

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References

  • Angulo CA, Angulo CU (2003) Study of the water quality and its relationship with white shrimp growing (Litopenaeus Vannamei), in the shrimp pond of Agua Verde S.A. de C.V. Rosario, Sin. M.Sc. Thesis, Universidad Autónoma de Sinaloa

  • Arredondo J, Ponce J (1998) Water quality in aquaculture. Ed. AGT SA

  • Australian Bureau of Statistics (ABS) (2003) A guide to interpreting time series—monitoring trends

  • Becerra M, Iano Y, Tarumoto M (2008) Evaluating some Yule-Walker Methods with the maximum-likelihood estimator for the spectral ARMA model. Tendências Matemática Aplicada Comput 9(2):175–184

    Google Scholar 

  • Bell T (1992) Principals of shrimp culture chemotherapy. In: Proceedings of the special session on shrimp farming. World Aquaculture Society, USA, pp 227–237

  • Boyd C, Musing Y (1992) Shrimp pond effluents: observations of the nature of the problem on commercial farms. In: Proceedings of the special session on shrimp farming. World Aquaculture Society, USA

  • Brockwell P, Davis R (1996) Introduction to time series and forecasting. Springer, New York

    Google Scholar 

  • Canadian Council of Ministers of the Environment (Canada) (CCME) (2004) An assessment of the application and testing of the water quality index of the Canadian Council of Ministers of the Environment for selected water bodies in Atlantic Canada. National indicators and reporting office. Available at: http://www.ec.gc.ca/soer-ree/N [Accessed August 2007]

  • Chapra S, Canale R (1999) Numerical methods for engineers. McGraw-Hill, México

    Google Scholar 

  • Chatfield C (2004) The analysis of time series: an introduction, 6th edn. Chapman & Hall/CRC, London

    Google Scholar 

  • Chien Y (1992) Water quality requeriments and management for marine shrimp culture. In: Proceedings of the special session on shrimp farming. World Aquaculture Society, USA, pp 144–156

  • Chow M (1997) Methodologies of using neural network and fuzzy logic technologies for motor incipient fault detection. World Scientific, Singapore

    Book  Google Scholar 

  • Cohen L (1995) Time–frequency signal analysis. Prentice Hall PTR, Englewood Cliffs

    Google Scholar 

  • Cohen J, Samocha T, Fox J (2005) Characterization of water quality factors during intensive raceway production of juvenile Litopenaeus vannamei using limited discharge and biosecure management tools. Aquacult Eng 32 Elsevier: 425–442

    Google Scholar 

  • De la Fuente D, García D (1998) Modelado de series temporales con métodos en bloque y recursivos. Desarrollo de estimadores y predictores adaptativos. Questiió 12:281–313

    Google Scholar 

  • Dijkhof W, Wensik E (2000) Small sample statistics of the yule-walker method for autoregressive parameter estimation. In: Proceedings of European signal processing conference

  • Emmanuel C (1993) Digital signal processing: a practical approach. Addison-Wesley, Reading

    Google Scholar 

  • Gutiérrez J (2004) Lógica difusa como herramienta para la bioindicación de la calidad del agua con macroinvertebrados acuáticos en la sabana de Bogotá–Colombia. Caldasia 26(1):161–172

    Google Scholar 

  • Gutiérrez J, Riss W, Ospina R (2006) Bioindicación de la calidad del agua en la sabana de Bogota–Colombia, mediante la utilización de la lógica difusa neuroadaptativa como herramienta. Limnología Caldasia 28(1):45–46

    Google Scholar 

  • Hirono Y (1992) Current practices of water quality management in shrimp farming and their limitations. In: Proceedings of the special session on shrimp farming. World Aquaculture Society, USA

  • Instituto Nacional de Ecología (INE) (2000) La calidad del agua en los ecosistemas costeros de México

  • Kenneth H (1998) Water quality prediction and probability network models. North Carolina State University, USA

  • Kenney J, Keeping E (1962) Mathematics of statistics, 3rd edn. Van Nostrand, Princeton

  • Li Y, Li J, Wang O (2006) The effects of dissolved oxygen concentration and stocking density on growth and non-specific immunity factors in chinese shrimp, Fenneropenaeus chinensis. Aquaculture 256 Elsevier:608–616

    Google Scholar 

  • Lin JY, Cheng CT, Chau KW (2006) Using support vector machines for long-term discharge prediction. Hydrolog Sci J 51(4):599–612

    Article  Google Scholar 

  • Martínez L (1994) Cultivo de camarones pendidos, Principios y prácticas, Ed. AGT Editor SA

  • Martínez L (1998) Ecología de los sistemas acuícolas. AGT Editor, México

    Google Scholar 

  • Muttil N, Chau KW (238) 2006. Neural network and genetic programming for modeling coastal algal blooms Int J Environ Pollution(28):3–4–223

    Google Scholar 

  • National Sanitation Foundation International (NSF) (2005). Available at: http://www.nsf.org [Accessed August 2007]

  • Navarro L, Mascarenhas A, Durazo R (1992) Una nota sobre la temperatura y salinidad de la capa superior del océano en la entrada del golfo de california en agosto 1992. Ciencias Marinas 23

  • Ocampo W, Ferré N, Domingo J, Schuhmacher M (2006) Assessing water quality in rivers with fuzzy inference systems: a case study. Environ Int 32 Elservier:733–742

    Google Scholar 

  • Páez OF (2001) Camaronicultura y medio ambiente. Instituto de Ciencias del mar y Limnología. UNAM, México, pp 271–298

  • Proakis J, Manolakis D (2007) Tratamiento digital de señales. Pearson Education, vol. 1, 4ª Ed. España

  • Samocha T (1992) Shrimp nursery systems and management. World Aquaculture Society, LA, pp 87–105

  • Secretaría de Medio Ambiente, Recursos Naturales y Pesca (SEMARNAP) (2006) NOM-001-ECOL-1996

  • Secretaría de Medio Ambiente y Recursos Naturales (SEMARNAT) (2005) Available at: http://www.semarnat.gob.mx. Accessed Aug 2007

  • Shumway R, Stoffer D (2000) Time series analysis and its applications. Springer, New York

    Google Scholar 

  • Soler V (2007) Lógica difusa aplicada a conjuntos imbalanceados: aplicación a la detección del síndrome de down. Departament de Microelectrònica i Sistemes Electrònics, Universitat Autònoma de Barcelona, Ph.D. Thesis

  • Yañez C, López I, De la Luz G (2008) Analysis and prediction of air quality data with the gamma classifier. Lecture Notes in Computer Science. Springer, New York, pp 651–658

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Acknowledgments

The authors of the present paper would like to thank the following institutions for their support in developing this work: National Polytechnic Institute, Mexico, Biology Research Centre of Sonora (CIB) and CONACyT.

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Authors and Affiliations

  1. Centre of Computer Research, National Polytechnic Institute, Av. Juan de Dios Bátiz s/n, Col. Nueva. Industrial Vallejo, Gustavo A. Madero, Mexico D.F., Mexico

    José Juan Carbajal Hernández, Luis P. Sánchez Fernández & Oleksiy Pogrebnyak

  2. Engineering Department, University of the Mexico Valley, Paseo de las Aves 1, San Mateo Nopala, Naucalpan, 53220, Estado de Mexico, Mexico

    José Juan Carbajal Hernández

Authors
  1. José Juan Carbajal Hernández
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  2. Luis P. Sánchez Fernández
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  3. Oleksiy Pogrebnyak
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Correspondence to José Juan Carbajal Hernández.

Appendix A

Appendix A

Canadian Council Ministers of Environment Water Quality Index

The percentage of the number of parameters whose objective limits are not met:

\( F_{1} = {\frac{{{\text{Number}}\;{\text{of}}\; {\text{failed}}\;{\text{variables}}}}{{{\text{total}}\;{\text{of}}\; {\text{number }}\;{\text{of}}\; {\text{variables}}}}} \times 100 \)

The percentage of individual tests that do not meet the objectives

\( F_{2} = {\frac{{{\text{Number}}\;{\text{of}}\;{\text{failed}}\;{\text{tests}}}}{{{\text{total}}\;{\text{of}}\;{\text{number}}\;{\text{of}}\;{\text{variables}}}}} \times 100 \)

When the test value must not exceed the objective:

\( {\text{excursion}}_{i} = {\frac{{{\text{objective}}_{i} }}{{{\text{Failed}}\;{\text{test}}\;{\text{value}}_{i} }}} - 1 \)

For the cases in which the test value must not fall below the objective:\( {\text{excursion}}_{i} = {\frac{{{\text{Failed}}\;{\text{test}}\;{\text{value}}}}{{{\text{objective}}_{i} }}} - 1 \)

normalized sum of excursions (nse) is calculated as \( {\text{nse}} = {\frac{{\mathop \sum \nolimits_{i = 1}^{n} {\text{excursion}}_{i} }}{{{\text{number }}\;{\text{of}}\;{\text{test}}}}} \)

Asymptotic function that scales the normalized sum of the excursions from objectives (nse) to yield a value between 0 and 100.

\( F_{3} = {\frac{\text{nse}}{{0.01{\text{nse}} + 0.01}}} \)

The CCME is calculated as:

\( {\text{CCME}} = 100 - \left( {{\frac{{\sqrt {F_{1}^{2} + F_{2}^{2} + F_{3}^{2} } }}{1.732}}} \right) \)

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Hernández, J.J.C., Fernández, L.P.S. & Pogrebnyak, O. Assessment and prediction of water quality in shrimp culture using signal processing techniques. Aquacult Int 19, 1083–1104 (2011). https://doi.org/10.1007/s10499-011-9426-z

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