Significance of Wavelet Transform and Data Mining in Forecasting Sea Level Time Series at the Pacific Entrance of the Panama Canal

Jose Antonio  Simmonds Sheppard; Masai Z. González; Agapito Ledezma; Juan Antonio Gómez H.

doi:10.48204/reict.v4n2.6747

Enviado janeiro 15, 2025

Publicado 2025-02-01

Artículos

v. 4 n. 2 (2025): REICIT

Significance of Wavelet Transform and Data Mining in Forecasting Sea Level Time Series at the Pacific Entrance of the Panama Canal

Jose Antonio Simmonds Sheppard ⁺⁻
Masai Z. González ⁺⁻
Agapito Ledezma ⁺⁻
Juan Antonio Gómez H. ⁺⁻

DOI https://doi.org/10.48204/reict.v4n2.6747

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DOI: 10.48204/reict.v4n2.6747

Publicado: 2025-02-01

Como Citar

Simmonds Sheppard , J. A., González , M. Z., Ledezma , A. e Gómez H. , J. A. (2025) “Significance of Wavelet Transform and Data Mining in Forecasting Sea Level Time Series at the Pacific Entrance of the Panama Canal”, REICIT, 4(2), p. 31–49. doi: 10.48204/reict.v4n2.6747.

Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Resumo

Understanding the tide level is crucial for safely navigating ships in harbors, managing sediment movement, and conducting environmental observations. This area has been actively researched for years, resulting in many proposed models to improve time series modeling and forecasting accuracy and efficiency. This study uses water level data recorded every hour from 1908-2007 at the Balboa Harbor tide gauge in Panama to create a model for predicting sea level changes. The data mining tool analyzes a range of time lags in the dataset to identify the best combinations. Analysis of the time-series data suggests that using cross-validation and a more extended lag period leads to more accurate forecasting. The study also proposes denoising the time series data as a pre-processing step and utilizing mining techniques with cross-validation and attribute selection evaluators for modeling sea-level changes at coastal areas characterized by nonlinearity and chaotic climatic changes.

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Referências

Albiach, J. C. C., Fanjul, E. A., Lahoz, M. G., Gomez, B. P., & Sanchez-Arevalo, I. R. g. (2000). Ocean forecasting in narrow shelf seas: application to the Spanish coasts. Coastal Engineering, 41(1-3), 269-293.
Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection.
Bergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192-213.
Bregman, J. I., & Makenthun, K. M. (2006). Environmental Impact Statements.
Cannas, B., Fanni, A., Sias, G., Tronci, S., & Zedda, M. K. (2005). River flow forecasting using neural networks and wavelet analysis. Geophys. Res. Abstr,
Cartwright, D. (1972). Secular changes in the oceanic tides at Brest, 1711–1936. Geophysical Journal International, 30(4), 433-449.
Cartwright, D. E., & Driver, J. (1971). Tides and waves in the vicinity of Saint Helena. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 270(1210), 603-646.
Chen, J., Shum, C., Wilson, C., Chambers, D., & Tapley, B. (2000). Seasonal sea level change from TOPEX/Poseidon observation and thermal contribution. Journal of Geodesy, 73, 638-647.
Deco, G., Neuneier, R., & Schümann, B. (1997). Non-parametric data selection for neural learning in non-stationary time series. Neural networks, 10(3), 401-407.
Douglas, B., Kearney, M. T., & Leatherman, S. P. (2000). Sea level rise: History and consequences. International Geophysics Series, Academic Press London, 75, 97-119.
Hamed, K. H., & Rao, A. R. (2000). Trend analysis by using wavelets. In Building Partnerships (pp. 1-10).
Haykin, S., & Network, N. (2004). A comprehensive foundation. Neural networks, 2(2004), 41.
Holgate, S. J., Matthews, A., Woodworth, P. L., Rickards, L. J., Tamisiea, M. E., Bradshaw, E., Foden, P. R., Gordon, K. M., Jevrejeva, S., & Pugh, J. (2013). New data systems and products at the permanent service for mean sea level. Journal of Coastal Research, 29(3), 493-504.
Kamruzzaman, J., Sarker, R., & Begg, R. (2007). Artificial Neural Networks. Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications: Concepts, Methodologies, Tools, and Applications, 222.
Kim, T. Y., Oh, K. J., Kim, C., & Do, J. D. (2004). Artificial neural networks for non-stationary time series. Neurocomputing, 61, 439-447.
Ki?i, Ö. (2009). Neural networks and wavelet conjunction model for intermittent streamflow forecasting. Journal of Hydrologic Engineering, 14(8), 773-782.
Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial intelligence, 97(1-2), 273-324.
Kunst, R. M., & Jumah, A. (2004). Toward a theory of evaluating predictive accuracy.
Labat, D., Mangin, A., & Ababou, R. (2002). Rainfall–runoff relations for karstic springs: multifractal analyses. Journal of Hydrology, 256(3-4), 176-195.
Long, N. C., & Meesad, P. (2014). An optimal design for type–2 fuzzy logic system using hybrid of chaos firefly algorithm and genetic algorithm and its application to sea level prediction. Journal of Intelligent & Fuzzy Systems, 27(3), 1335-1346.
Meyer, Y. (1993). Wavelets: algorithms & applications. Philadelphia: SIAM (Society for Industrial and Applied Mathematics.
Misiti, M., Misiti, Y., Oppenheim, G., & Poggi, J.-M. (1996). Wavelet toolbox. The MathWorks Inc., Natick, MA, 15, 21.
Monbaliu, J., Padilla-Hernandez, R., Hargreaves, J. C., Albiach, J. C. C., Luo, W., Sclavo, M., & Guenther, H. (2000). The spectral wave model, WAM, adapted for applications with high spatial resolution. Coastal Engineering, 41(1-3), 41-62.
Pashova, L., & Popova, S. (2011). Daily sea level forecast at tide gauge Burgas, Bulgaria using artificial neural networks. Journal of sea research, 66(2), 154-161.
Peralta, J., Gutierrez, G., & Sanchis, A. (2010). Time series forecasting by evolving artificial neural networks using genetic algorithms and estimation of distribution algorithms. The 2010 international joint conference on neural networks (IJCNN),
Quinlan, J. R. (1992). Induction of decision trees. Machine learning, 1, 181-186.
Rosso, O., Figliola, A., Blanco, S., & Jacovkis, P. (2004). Signal separation with almost periodic components: a wavelets based method. Revista mexicana de física, 50(2), 179-186.
Rourke, F. O., Boyle, F., & Reynolds, A. (2010). Tidal energy update 2009. Applied Energy, 87(2), 398-409. https://doi.org/10.1016/j.apenergy.2009.08.014
Rushing, J., Ramachandran, R., Nair, U., Graves, S., Welch, R., & Lin, H. (2005). ADaM: a data mining toolkit for scientists and engineers. Computers & geosciences, 31(5), 607-618.
Simmonds, J. A., Gomez, J. A., & Ledezma, A. (2017). Forecasting sea level changes applying data mining techniques to the Cristobal Bay time series, Panama. Journal of Water and Climate Change, 8(1), 89-101. https://doi.org/10.2166/wcc.2016.041
Umgiesser, G., Canu, D. M., Cucco, A., & Solidoro, C. (2004). A finite element model for the Venice Lagoon. Development, set up, calibration and validation. Journal of Marine Systems, 51(1-4), 123-145.
Zhang, G. P. (2007). A neural network ensemble method with jittered training data for time series forecasting. Information Sciences, 177(23), 5329-5346.

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Este obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional.

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[1] Albiach, J. C. C., Fanjul, E. A., Lahoz, M. G., Gomez, B. P., & Sanchez-Arevalo, I. R. g. (2000). Ocean forecasting in narrow shelf seas: application to the Spanish coasts. Coastal Engineering, 41(1-3), 269-293.

[2] Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection.

[3] Bergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192-213.

[4] Bregman, J. I., & Makenthun, K. M. (2006). Environmental Impact Statements.

[5] Cannas, B., Fanni, A., Sias, G., Tronci, S., & Zedda, M. K. (2005). River flow forecasting using neural networks and wavelet analysis. Geophys. Res. Abstr,

[6] Cartwright, D. (1972). Secular changes in the oceanic tides at Brest, 1711–1936. Geophysical Journal International, 30(4), 433-449.

[7] Cartwright, D. E., & Driver, J. (1971). Tides and waves in the vicinity of Saint Helena. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 270(1210), 603-646.

[8] Chen, J., Shum, C., Wilson, C., Chambers, D., & Tapley, B. (2000). Seasonal sea level change from TOPEX/Poseidon observation and thermal contribution. Journal of Geodesy, 73, 638-647.

[9] Deco, G., Neuneier, R., & Schümann, B. (1997). Non-parametric data selection for neural learning in non-stationary time series. Neural networks, 10(3), 401-407.

[10] Douglas, B., Kearney, M. T., & Leatherman, S. P. (2000). Sea level rise: History and consequences. International Geophysics Series, Academic Press London, 75, 97-119.

[11] Hamed, K. H., & Rao, A. R. (2000). Trend analysis by using wavelets. In Building Partnerships (pp. 1-10).

[12] Haykin, S., & Network, N. (2004). A comprehensive foundation. Neural networks, 2(2004), 41.

[13] Holgate, S. J., Matthews, A., Woodworth, P. L., Rickards, L. J., Tamisiea, M. E., Bradshaw, E., Foden, P. R., Gordon, K. M., Jevrejeva, S., & Pugh, J. (2013). New data systems and products at the permanent service for mean sea level. Journal of Coastal Research, 29(3), 493-504.

[14] Kamruzzaman, J., Sarker, R., & Begg, R. (2007). Artificial Neural Networks. Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications: Concepts, Methodologies, Tools, and Applications, 222.

[15] Kim, T. Y., Oh, K. J., Kim, C., & Do, J. D. (2004). Artificial neural networks for non-stationary time series. Neurocomputing, 61, 439-447.

[16] Ki?i, Ö. (2009). Neural networks and wavelet conjunction model for intermittent streamflow forecasting. Journal of Hydrologic Engineering, 14(8), 773-782.

[17] Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial intelligence, 97(1-2), 273-324.

[18] Kunst, R. M., & Jumah, A. (2004). Toward a theory of evaluating predictive accuracy.

[19] Labat, D., Mangin, A., & Ababou, R. (2002). Rainfall–runoff relations for karstic springs: multifractal analyses. Journal of Hydrology, 256(3-4), 176-195.

[20] Long, N. C., & Meesad, P. (2014). An optimal design for type–2 fuzzy logic system using hybrid of chaos firefly algorithm and genetic algorithm and its application to sea level prediction. Journal of Intelligent & Fuzzy Systems, 27(3), 1335-1346.

[21] Meyer, Y. (1993). Wavelets: algorithms & applications. Philadelphia: SIAM (Society for Industrial and Applied Mathematics.

[22] Misiti, M., Misiti, Y., Oppenheim, G., & Poggi, J.-M. (1996). Wavelet toolbox. The MathWorks Inc., Natick, MA, 15, 21.

[23] Monbaliu, J., Padilla-Hernandez, R., Hargreaves, J. C., Albiach, J. C. C., Luo, W., Sclavo, M., & Guenther, H. (2000). The spectral wave model, WAM, adapted for applications with high spatial resolution. Coastal Engineering, 41(1-3), 41-62.

[24] Pashova, L., & Popova, S. (2011). Daily sea level forecast at tide gauge Burgas, Bulgaria using artificial neural networks. Journal of sea research, 66(2), 154-161.

[25] Peralta, J., Gutierrez, G., & Sanchis, A. (2010). Time series forecasting by evolving artificial neural networks using genetic algorithms and estimation of distribution algorithms. The 2010 international joint conference on neural networks (IJCNN),

[26] Quinlan, J. R. (1992). Induction of decision trees. Machine learning, 1, 181-186.

[27] Rosso, O., Figliola, A., Blanco, S., & Jacovkis, P. (2004). Signal separation with almost periodic components: a wavelets based method. Revista mexicana de física, 50(2), 179-186.

[28] Rourke, F. O., Boyle, F., & Reynolds, A. (2010). Tidal energy update 2009. Applied Energy, 87(2), 398-409. https://doi.org/10.1016/j.apenergy.2009.08.014

[29] Rushing, J., Ramachandran, R., Nair, U., Graves, S., Welch, R., & Lin, H. (2005). ADaM: a data mining toolkit for scientists and engineers. Computers & geosciences, 31(5), 607-618.

[30] Simmonds, J. A., Gomez, J. A., & Ledezma, A. (2017). Forecasting sea level changes applying data mining techniques to the Cristobal Bay time series, Panama. Journal of Water and Climate Change, 8(1), 89-101. https://doi.org/10.2166/wcc.2016.041

[31] Umgiesser, G., Canu, D. M., Cucco, A., & Solidoro, C. (2004). A finite element model for the Venice Lagoon. Development, set up, calibration and validation. Journal of Marine Systems, 51(1-4), 123-145.

[32] Zhang, G. P. (2007). A neural network ensemble method with jittered training data for time series forecasting. Information Sciences, 177(23), 5329-5346.

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