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Acta Geophysica

, Volume 61, Issue 6, pp 1522–1537 | Cite as

Investigating prediction performance of an artificial neural network and a numerical model of the tidal signal at Puerto Belgrano, Bahia Blanca Estuary (Argentina)

  • Jorge O. Pierini
  • Michele Lovallo
  • Luciano Telesca
  • Eduardo A. Gómez
Research Article

Abstract

In the present study we compare performances of the prediction of hourly tidal level variations at Puerto Belgrano, a coastal site in the Bahia Blanca Estuary (Argentina), by means of the MOHID model, which is a numerical model designed for coastal and estuarine shallow water applications, and of an artificial neural network (ANN). It was shown that the ANN model is able to predict the hourly tidal levels over long term duration with at least seven days of observations and with a better performance in respect to the numerical model. Our findings can be useful to implement ANN-based tools for future studies of the hydrodynamics of Bahía Blanca estuary.

Key words

artificial neural networks hydrodinamic model tides 

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References

  1. Campuzano, F., J.O. Pierini, and P. Leitão (2008), Hydrodynamics and sediments in Bahía Blanca estuary. Data analysis and modelling. In: R. Neves, J. Baretta, and M. Mateus (eds.), Perspectives on Integrated Coastal Zone Management in South America, IST Scientific Publishers, Lisbon, 483–503.Google Scholar
  2. Chapra, S.C. (1997), Surface Water Quality Modeling, Water Resources and Environmental Engineering Series, McGraw-Hill, New York, 850 pp.Google Scholar
  3. Deo, M.C., and G. Chaudhari (1998), Tide prediction using neural networks, Comput. Aided Civil Infrastruc. Eng. 13,2, 113–120, DOI: 10.1111/0885-9507.00091.CrossRefGoogle Scholar
  4. Fahlman, S.E. (1988), An empirical study of learning speed in back-propagation networks, Technical report CMU-CS-88-162, Carnegie-Mellon University, Computer Science Dept., Pittsburgh, USA.Google Scholar
  5. Grubert, J. (1995), Prediction of estuarine instabilities with artificial neural networks, J. Comput. Civil Eng. ASCE 9,4, 266–274, DOI: 10.1061/(ASCE)0887-3801(1995)9:4(266).CrossRefGoogle Scholar
  6. Huang, W., C. Murray, N. Kraus, and J. Rosati (2003), Development of a regional neural network for coastal water level predictions, Ocean Eng. 30,17, 2275–2295, DOI: 10.1016/S0029-8018(03)00083-0.CrossRefGoogle Scholar
  7. IOC, IHO, BODC (2003), Centenary edition of the GEBCO digital atlas, Intergovernmental Oceanographic Commission and the International Hydrographic Organization as part of the General Bathymetric Chart of the Oceans; British Oceanographic Data Centre, Liverpool (CD-ROM).Google Scholar
  8. Jacobs, R.A. (1988), Increased rates of convergence through learning rate adaptation, Neural Network 1,4, 295–307, DOI: 10.1016/0893-6080(88) 90003-2.CrossRefGoogle Scholar
  9. Lee, T.-L. (2004), Back-propagation neural network for long-term tidal predictions, Ocean Eng. 31,2, 225–238, DOI: 10.1016/S0029-8018(03)00115-X.CrossRefGoogle Scholar
  10. Leitão, P.C. (2003), Integration of scales and processes in the marine environment modelling, Ph.D. Thesis, Technical Superior Institute, Lisbon.Google Scholar
  11. Lovallo, M., J.O. Pierini, and L. Telesca (2012), Power spectrum and Fisher-Shannon information plane analysis of tidal records, Physica A 391,20, 4711–4719, DOI: 10.1016/j.physa.2012.05.047.CrossRefGoogle Scholar
  12. Makarynskyy, O. (2004), Improving wave predictions with artificial neural networks, Ocean Eng. 31,5–6, 709–724, DOI: 10.1016/j.oceaneng.2003.05.003.CrossRefGoogle Scholar
  13. Martins, F., P.C. Leitão, A. Silva, and R. Neves (2001), 3D modelling in the Sado estuary using a new generic vertical discretization approach, Oceanol. Acta 24, Suppl. 1, 51–62, DOI: 10.1016/S0399-1784(01)00092-5.CrossRefGoogle Scholar
  14. Mase, H. (1995), Evaluation of artificial armour layer stability by neural network method. In: Proc. 26th Congress of IAHR, London, Int. Assoc. Hydraul. Res., The Netherlands, 341–346.Google Scholar
  15. Mase, H., M. Sakamoto, and T. Sakai (1995), Neural network for stability analysis of rubble-mound breakwaters, J. Waterw. Port Coast. Ocean Eng. ASCE 121,6, 294–299, DOI: 10.1061/(ASCE)0733-950X(1995)121:6(294).CrossRefGoogle Scholar
  16. Mourre, B., L. Crosnier, and C. Le Provost (2006), Real-time sea-level gauge observations and operational oceanography, Philos. Trans. Roy. Soc. A 364,1841, 867–884, DOI: 10.1098/rsta.2006.1743.CrossRefGoogle Scholar
  17. Pierini, J.O. (2007), Circulación y transporte en zonas costeras del estuario de Bahía Blanca, Ph.D. Thesis, Universidad de Buenos Aires, Buenos Aires, 225 pp (in Spanish).Google Scholar
  18. Pierini, J.O., and E. Gómez (2009), Tidal forecasting using RNN in Bahia Blanca estuary, Argentina, Interciencia 34,12, 851–856.Google Scholar
  19. Pierini, J.O., J.E. Marcovecchio, F. Campuzano, and G.M.E. Perillo (2008a), Evolution of salinity and temperature in Bahía Blanca estuary, Argentina. In: R. Neves, J.W. Baretta, and M. Mateus (eds.), Perspectives on Integrated Coastal Zone Management in South America, IST Scientific Publishers, Lisbon, 505–513.Google Scholar
  20. Pierini, J.O., F. Campuzano, J. Marcovecchio, and G.M.E. Perillo (2008b), The application of MOHID to assess the potential effect of sewage discharge system at Bahía Blanca estuary (Argentina). In: R. Neves, J.W. Baretta, and M. Mateus (eds.), Perspectives on Integrated Coastal Zone Management in South America, IST Scientific Publishers, Lisbon, 515–522.Google Scholar
  21. Pierini, J.O., J. Marcovecchio, F. Campuzano, and G.M.E. Perilo (2008c), MOHID oil spill modelling in coastal zones: A case study in Bahía Blanca estuary (Argentina). In: R. Neves, J.W. Baretta, and M. Mateus (eds.), Perspectives on Integrated Coastal Zone Management in South America, IST Scientific Publishers, Lisbon, IST Scientific Publishers, 523–528.Google Scholar
  22. Pierini, J.O., M.E. Streitenberger, and M.D. Baldini (2012), Evaluation of faecal contamination in Bahía Blanca estuary (Argentina) using a numerical model, Rev. Biol. Mar. Oceanogr. 47,2, 193–202.CrossRefGoogle Scholar
  23. Rumelhart, D.E., G.E. Hinton, and R.J. Williams (1986), Learning representations by back-propagating errors, Nature 323,6088, 533–536, DOI: 10.1038/323533a0.CrossRefGoogle Scholar
  24. Tsai, C.-P., and T.-L. Lee (1999), Back-propagation neural network in tidal-level forecasting, J. Waterw. Port Coast. Ocean Eng. ASCE 125,4, 195–202, DOI: 10.1061/(ASCE)0733-950X(1999)125:4(195).CrossRefGoogle Scholar
  25. Vaziri, M. (1997), Predicting Caspian Sea surface water level by ANN and ARIMA models, J. Waterw. Port Coast. Ocean Eng. ASCE 123,4, 158–162, DOI: 10.1061/(ASCE)0733-950X(1997)123:4(158).CrossRefGoogle Scholar
  26. Williams, T.P. (1994), Predicting changes in construction cost indexes using neural networks, J. Constr. Eng. Mgmt. ASCE 120,2, 306–320, DOI: 10.1061/(ASCE)0733-9364(1994)120:2(306).CrossRefGoogle Scholar
  27. Willmott, C.J. (1982), Some comments on the evaluation of model performance, Bull. Am. Meteorol. Soc. 63,11, 1309–1369, DOI: 10.1175/1520-0477 (1982)063〈1309:SCOTEOt〉2.0.CO;2.CrossRefGoogle Scholar

Copyright information

© Versita Warsaw and Springer-Verlag Wien 2013

Authors and Affiliations

  • Jorge O. Pierini
    • 1
  • Michele Lovallo
    • 2
  • Luciano Telesca
    • 3
  • Eduardo A. Gómez
    • 4
  1. 1.Comisión de Investigaciones CientíficasUniversidad Nacional del Sur — Consejo Nacional de Investigaciones Científicas y TécnicasBahía BlancaArgentina
  2. 2.Agenzia Regionale per la Protezione dell’Ambiente (ARPAB)PotenzaItaly
  3. 3.National Research CouncilInstitute of Methodologies for Environmental AnalysisTitoItaly
  4. 4.Centro Cientifico TecnologicoUniversidad Nacional del Sur — Consejo Nacional de Investigaciones Científicas y TécnicasBahía BlancaArgentina

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