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Forecasting Local Mean Sea Level by Generalized Behavioral Learning Method

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Abstract

Determining and forecasting the local mean sea level (MSL), which is a major indicator of global warming, is an essential issue to set public policies to save our future. Owing to its importance, MSL values are measured and shared periodically by many agencies. It is not easy to model or forecast MSL because it depends on many dynamic sources such as global warming, geophysical phenomena, and circulations in the ocean and atmosphere. Several of researchers applied and recommended employing artificial neural network (ANN) in the estimation of MSL. However, ANN does not take into account the order of samples, which may consist essential information. In this study, the generalized behavioral learning method (GBLM), which is based on behavioral learning theories, was employed in order to achieve higher accuracies by using samples in the training dataset and the order of samples. To evaluate and validate GBLM, MSL of seven stations around the world was picked up. These datasets were employed to forecast the local MSL for the future. Obtained results were compared with the ones obtained by ANN that is trained by extreme learning machine and the literature. The GBLM is found to be successful in terms of the achieved high accuracies and the ability to tracking trends and fluctuations of a local MSL.

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References

  1. Parker, B.B.: Sea level as an indicator of climate and global change. Mar. Technol. Soc. J. 25(4), 13–24 (1992)

    Google Scholar 

  2. El-Shazly, A.H.: The efficiency of neural networks to model and predict monthly mean sea level from short spans applied to Alexandria tide gauge. In: Pharaohs to Geoinformatics, FIG Working Week and GSDI-8 (2005)

  3. Fankhauser, S.: Protection versus retreat: the economic costs of sea-level rise. Environ. Plan. A 27(2), 299–319 (1995)

    Article  Google Scholar 

  4. Pashova, L.; Popova, S.: Daily sea level forecast at tide gauge Burgas, Bulgaria using artificial neural networks. J. Sea Res. 66(2), 154–161 (2011)

    Article  Google Scholar 

  5. Church, J.; White, N.; Aarup, T.; Wilson, W.; Woodworth, P.; Domingues, C.; Hunter, J.; Lambeck, K.: Understanding global sea levels: past, present and future. Sustain. Sci. 3(1), 9–22 (2008)

    Article  Google Scholar 

  6. IPCC. Climate change 2007: the physical science basis. In: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge (2007)

  7. Lemke, P.; Ren, J.; Alley, R.; Allison, I.; Carrasco, J.; Flato, G.; Fujii, Y.; Kaser, G.; Mote, P.; Thomas, R.; Zhang, T.: Climate change: the physical science basis. In: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, pp. 337–383. Cambridge University Press (2007)

  8. Church, J.; White, N.: A 20th century acceleration in global sea-level rise. Geophys. Res. Lett. 33(L01602) (2006)

  9. Lebedev, S.A.: Climatic change of the Baltic Sea level and sea surface temperature based on satellite altimetry and radiometry. In: Baltic International Symposium (BALTIC) (2014)

  10. Willis, J.K.; Chambers, D.P.; Nerem, R.S.: Assessing the globally averaged sea level budget on seasonal to interannual timescales. J. Geophys. Res. Oceans 113, C06015 (2008)

    Google Scholar 

  11. Sun, X.; Lv, T.; Gao, Y.; Su, F.; Zhang, T.; Fan, J.: Quantitative analysis of sea level rise caused by continental input. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 515–518 (2012)

  12. Stankeviciute, L.; Criqui, P.: Energy and climate policies to 2020: the impacts of the European “20/20/20” approach. Int. J. Energy Sect. Manag. 2(2), 252–273 (2008)

    Article  Google Scholar 

  13. Mitchum, G.T.; Kilonsky, B.J.; Miyamoto, B.M.: Methods for maintaining a stable datum in a sea level monitoring system. Proc. Oceans Eng. Today’s Technol. Tomorrow’s Preserv. 1, 1–25 (1994)

    Google Scholar 

  14. Tseng, Y.-T.; Ding, J.-J.; Lou, J.-Y.: Global sea level change in the past century. In: IEEE OCEANS 2014—TAIPEI (2014)

  15. Ghorbani, M.A.; Khatibi, R.; Aytek, A.; Makarynskyy, O.; Shiri, J.: Sea water level forecasting using genetic programming and comparing the performance with artificial neural networks. Comput. Geosci. 36(5), 620–627 (2010)

    Article  Google Scholar 

  16. Hunter, J.: Estimating sea-level extremes under conditions of uncertain sea-level rise. Clim. Change 99, 331–350 (2010)

    Article  Google Scholar 

  17. Sztobryn, M.: Application of artificial neural network into the water level modeling and forecast. TransNav Int. J. Mar. Navig. Saf. Sea Transp. 7(2), 219–223 (2013)

    Article  Google Scholar 

  18. Ertuğrul, O.F.: A novel approach for extracting ideal exemplars by clustering for massive time-ordered datasets. Turk. J. Electr. Eng. Comput. Sci. (In Press). doi:10.3906/elk-1602-341

  19. Zhang, G.; Patuwo, B.E.; Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14(1), 35–62 (1998)

    Article  Google Scholar 

  20. Bouton, M.E.; Moody, E.W.: Memory processes in classical conditioning. Neurosci. Biobehav. Rev. 28, 663–674 (2004)

    Article  Google Scholar 

  21. Schunk, D.H.: Learning Theories an Educational Perspective, 6th edn. Pearson, London (2012)

    Google Scholar 

  22. Ertuğrul, Ö.F.; Tagluk, M.E.: A novel machine learning method based on generalized behavioral learning theory. Neural Comput. Appl. (2016). doi:10.1007/s00521-016-2314-8

    Google Scholar 

  23. Huang, G.-B.; Zhu, Q.-Y.; Siew, C.-K.: Extreme learning machine: a new learning scheme of feed forward neural networks. Neural Netw. 2, 985–990 (2004)

    Google Scholar 

  24. Huang, G.-B.; Zhu, Q.-Y.; Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)

    Article  Google Scholar 

  25. Kaya, Y.; Kayci, L.; Tekin, R.; Ertuğrul, Ö.F.: Evaluation of texture features for automatic detecting butterfly species using extreme learning machine. J. Exp. Theor. Artif. Intell. 26(2), 267–281 (2014)

    Article  Google Scholar 

  26. http://www.psmsl.org

  27. Karimi, S.; Kisi, O.; Shiri, J.; Makarynskyy, O.: Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia. Comput. Geosci. 52, 50–59 (2013)

    Article  Google Scholar 

  28. Makarynskyy, O.; Makarynska, D.; Kuhn, M.; Featherstone, W.E.: Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia. Estuar. Coast. Shelf Sci. 61(2), 351–360 (2004)

    Article  Google Scholar 

  29. Filippo, A.; Torres, A.R.; Kjerfve, B.; Monat, A.: Application of artificial neural network (ANN) to improve forecasting of sea level. Ocean Coast. Manag. 55, 101–110 (2012)

    Article  Google Scholar 

  30. Ertuğrul, O.F.; Kaya, Y.: A detailed analysis on extreme learning machine and novel approaches based on ELM. Am. J. Comput. Sci. Eng. 1(5), 43–50 (2014)

    Google Scholar 

  31. http://tidesandcurrents.noaa.gov/sltrends/sltrends_global_station.htm?stnid=010-001

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Batman University, 72060, Batman, Turkey

    Ömer Faruk Ertuğrul

  2. Department of Electrical and Electronics Engineering, Inonu University, 44060, Malatya, Turkey

    Mehmet Emin Tağluk

Authors
  1. Ömer Faruk Ertuğrul
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  2. Mehmet Emin Tağluk
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Correspondence to Ömer Faruk Ertuğrul.

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Ertuğrul, Ö.F., Tağluk, M.E. Forecasting Local Mean Sea Level by Generalized Behavioral Learning Method. Arab J Sci Eng 42, 3289–3298 (2017). https://doi.org/10.1007/s13369-017-2468-4

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  • DOI: https://doi.org/10.1007/s13369-017-2468-4

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