Modeling inflow rates for the water exchange management in semi-intensive aquaculture ponds

Aquacultural Engineering

Volumn 48, Issue , 2012, Pages 19-30

  Gutiérrez Estrada, Juan Carlos ^a   Pulido Calvo, Inmaculada ^a   De la Rosa, Ignacio ^a,b   Marchini, Bruno ^b  

^a UNIVERSITY OF HUELVA   (Spain)

^b Langostinos de Huelva SA   (Spain)

Author keywords

Artificial neural network; Evolutionary algorithm; Generalized additive model; Multiple fuzzy regression; Multiple linear regression; Semi intensive fishfarm; Southern spain; Sparus aurata

Indexed keywords

FUZZY REGRESSIONS; GENERALIZED ADDITIVE MODEL; MULTIPLE LINEAR REGRESSIONS; SEMI-INTENSIVE FISHFARM; SOUTHERN SPAIN; SPARUS AURATA;

BIOCHEMICAL OXYGEN DEMAND; DECISION MAKING; EVOLUTIONARY ALGORITHMS; FUZZY LOGIC; KNOWLEDGE MANAGEMENT; LINEAR REGRESSION; NEURAL NETWORKS; TURBIDITY; WATER QUALITY;

LAKES;

ALGORITHM; AMMONIA; ARTIFICIAL NEURAL NETWORK; CONTROL SYSTEM; CORRELATION; DECISION MAKING; FUZZY MATHEMATICS; INTENSIVE CULTURE; LINEARITY; NONLINEARITY; PERCIFORM; POND; TURBIDITY; WATER EXCHANGE; WATER QUALITY; WATER TEMPERATURE;

SPAIN;

SPARUS AURATA;

EID: 84858699242     PISSN: 01448609     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aquaeng.2011.12.009     Document Type: Article

References (43)

1. Anctil F., Rat A. Evaluation of neural network streamflow forecasting on 47 watersheds. J. Hydrol. Eng. 2005, 10(1):85-88.
2. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology Artificial neural networks in hydrology. I. Preliminary concepts. J. Hydrol. Eng. 2000, 5(2):115-123.
3. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology Artificial neural networks in hydrology. II. Hydrologic applications. J. Hydrol. Eng. 2000, 5(2):124-137.
4. Boyd C.E., Tucker C.S. Pond Aquaculture Water Quality Management 1998, Kluwer Academic Publishers, MA, USA.
5. Chen D.G., Hargreaves N.B., Ware D.M., Liu Y. A fuzzy logic model with genetic algorithm for analyzing fish stock-recruitment relationship. Can. J. Fish. Aquat. Sci. 2000, 57:1878-1887.
6. Diana J.S., Szyper J.P., Batterson T.R., Boyd C.E., Piedrahita R.H. Water Quality in Ponds. Dynamics of Pond Aquaculture 1997, 3:53-71. CRC Press, Boca Raton, FL, USA.
7. Dubrovin T., Jolma A., Turunen E. Fuzzy model for real-time reservoir operation. J. Water Resour. Plan. Manage. 2002, 128(1):66-73.
8. Fernando T.M.K.G., Maier H.R., Dandy G.C. Selection of input variables for data driven models: an average shifted histogram partial mutual information estimator approach. J. Hydrol. 2009, 367:165-176.
9. Goldberg D. Genetic Algorithms in Search, Optimization and Machine Learning 1989, Addison-Wesley, Reading, MA, USA.
10. Gutiérrez-Estrada J.C., de Pedro-Sanz E., López-Luque R., Pulido-Calvo I. Comparison between traditional methods and artificial neural networks for ammonia concentration forescasting in an eel (Anguilla anguilla L.) intensive rearing system. Aquacult. Eng. 2004, 31:183-203.
11. Gutiérrez-Estrada J.C., Silva C., Yáñez E., Rodríguez N., Pulido-Calvo I. Monthly catch forecasting of anchovy Engraulis ringens in the north area of Chile: non-linear univariate approach. Fish. Res. 2007, 86:188-200.
12. Gutiérrez-Estrada J.C., Vasconcelos R., Costa M.J. Estimating fish community diversity from environmental features in the Tagus estuary (Portugal): multiple linear regression and artificial neural network approaches. J. Appl. Ichthyol. 2008, 24:150-162.
13. Gutiérrez-Estrada J.C., Yáñez E., Pulido-Calvo I., Silva C., Plaza F., Bórquez C. Pacific sardine (Sardinops sagax, Jenyns 1842) landings prediction: a neural network ecosystemic approach. Fish. Res. 2009, 100:116-125.
14. Hastie T.J., Tibshirani R.J. Generalized Additive Models 1990, Chapman & Hall/CRC, Boca Raton, FL, USA.
15. Holland J. Adaptation in Natural and Artificial Systems 1975, MIT Press, Cambridge, MA, USA.
16. Hunter A., Kennedy L., Henry J., Ferguson I. Application of neural networks and sensitivity analysis to improved prediction of trauma survival. Comput. Methods Programs Biomed. 2000, 62:11-19.
17. Hsu K., Gupta H.V., Sorooshian S. Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res. 1995, 31(10):2517-2530.
18. Iyer M.S., Rhinehart R.R. A method to determine the required number of neural-network training repetitions. IEEE Trans. Neural Netw. 1999, 10(2):427-432.
19. Jacob C. Illustrating Evolutionary Computation with Mathematica 2001, Morgan Kaufmann Publishers, San Francisco, CA, USA.
20. Jang J.S.R., Sun C.T., Mizutani E. Neuro-Fuzzy and Soft Computing 1997, Prentice Hall, NJ, USA.
21. Kelly A.M., Kohler C.C. Climate, site, and pond design. Dynamics of Pond Aquaculture 1997, 5:109-133. CRC Press, Boca Raton, FL, USA.
22. Kosko B. Fuzzy Engineering 1997, Prentice Hall, London, UK.
23. Lee P.G., Lea R.N., Dohmann E., Prebilsky W., Turk P.E., Ying H., Whitson J.L. Denitrification in aquaculture systems: an example of a fuzzy logic control problem. Aquacult. Eng. 2000, 23:37-59.
24. Legates D.R., McCabe G.J. Evaluating the use of 'goodness-of-fit' measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 1999, 35(1):233-241.
25. Lekang O.I. Aquaculture Engineering 2007, Blackwell Publishing, Oxford, UK.
26. Li Y., Jiao Y., He Q. Decreasing uncertainty in catch rate analyses using Delta-AdaBoost: An alternative approach in catch and bycatch analyses with high percentage of zeros. Fish. Res. 2011, 107:261-271.
27. Makkeasorn A., Chang N.B., Zhou X. Short-term streamflow forecasting with global climate change implications-a comparative study between genetic programming and neural network models. J. Hydrol. 2008, 352:336-354.
28. Munson B.R., Young D.F., Okiishi T.H. Fundamentals of Fluid Mechanics 2006, John Wiley & Sons, USA.
29. Murase H., Nagashima H., Yonezaki S., Matsukura R., Kitakado T. Application of a generalized additive model (GAM) to reveal relationships between environmental factors and distributions of pelagic fish and krill: a case study in Sendai Bay, Japan. ICES J. Mar. Sci. 2009, 66:1417-1424.
30. Ochoa-Rivera J.C., Andreu J., García-Bartual R. Influence of inflows modeling on management simulation of water resources system. J. Water Resour. Plan. Manage. 2007, 133(2):106-115.
31. Papandroulakis N., Markakis G., Divanach P., Kentouri M. Feeding requirements of sea bream (Sparus aurata) larvae under intensive rearing conditions-development of a fuzzy logic controller for feeding. Aquacult. Eng. 2000, 21(4):285-299.
32. Poxton M.G., Allouse S.B. Water quality criteria for marine fisheries. Aquacult. Eng. 1982, 1:153-191.
33. Pulido-Calvo I., Gutiérrez-Estrada J.C. Improved irrigation water demand forecasting using a soft-computing hybrid model. Biosyst. Eng. 2009, 102:202-218.
34. Pulido-Calvo I., Portela M.M. Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds. J. Hydrol. 2007, 332:1-15.
35. Ruiz-Velazco J.M.J., Hernández-Llamas A., Gómez-Muñoz V.M. Management of stocking density, pond size, starting time of aeration, and duration of cultivation for intensive commercial production of shrimp Litopenaeus vannamei. Aquacult. Eng. 2010, 43:114-119.
36. Rumelhart D.E., Hinton G.E., Williams R.J. 'Learning' representations by backpropagation errors. Nature 1986, 323:533-536.
37. Shrestha R.R., Theobald S., Nestmann F. Simulation of flood flow in a river system using artificial neural networks. Hydrol. Earth Sits. Sci. 2005, 9(4):313-321.
38. Soto-Zarazúa G.M., Penuche-Vera R., Rico-García E., Toledano-Ayala M., Ocampo-Velázquez R., Herrera-Ruiz G. An automated recirculation aquaculture system based on fuzzy logic control for aquaculture production of tilapia (Oreochromis niloticus). Aquacult. Int. 2011, 19:797-808.
39. Steeby J.A., Hargreaves J.A., Tucker C.S., Cathcart T.P. Modeling industry-wide sediment oxygen demand and estimation of the contribution of sediment to total respiration in commercial channel catfish ponds. Aquacult. Eng. 2004, 31:247-262.
40. Tan Y., van Cauwenberghe A. Neural-network-based d-step-ahead predictors for nonlinear systems with time delay. Eng. Appl. Artif. Intel. 1999, 12(1):21-25.
41. Yi Y., Lin C.D., Diana J.S. Techniques to mitigate clay turbidity problems in fertilized earthen fish ponds. Aquacult. Eng. 2003, 27:39-51.
42. Yu R., Leung P., Bienfang P. Predicting shrimp growth: artificial neural network versus nonlinear regression models. Aquacult. Eng. 2006, 34:26-32.
43. Zeldis D., Prescott S. Fish disease diagnosis program-problems and some solutions. Aquacult. Eng. 2000, 23:3-11.