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 enero 15, 2025

Publicado 2025-02-01

Artículos

Vol. 4 Núm. 2 (2025): REICIT

Importancia de la Transformada de Ondoletas y Minería de Datos para Pronosticar el Nivel del Mar de una Serie Temporal en la Entrada Pacífico del Canal de Panamá

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

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

PDF

Citas

DOI: 10.48204/reict.v4n2.6747

Publicado: 2025-02-01

Cómo citar

Simmonds Sheppard , J. A., González , M. Z., Ledezma , A. y Gómez H. , J. A. (2025) «Importancia de la Transformada de Ondoletas y Minería de Datos para Pronosticar el Nivel del Mar de una Serie Temporal en la Entrada Pacífico del Canal de Panamá», REICIT, 4(2), pp. 31–49. doi: 10.48204/reict.v4n2.6747.

Derechos de autor 2025 REICIT

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.

Resumen

Comprender el nivel de las mareas es crucial para navegar con seguridad en los puertos, gestionar el movimiento de sedimentos y realizar observaciones ambientales. Esta área ha sido investigada activamente durante años, lo que dio como resultado muchos modelos propuestos para mejorar la precisión y eficiencia de los modelos de series temporales y los pronósticos. Este estudio utiliza datos del nivel del agua registrados cada hora desde 1908 hasta 2007 en el mareógrafo del puerto de Balboa en Panamá para crear un modelo para predecir los cambios en el nivel del mar. La herramienta de minería de datos analiza una variedad de desfases temporales en el conjunto de datos para identificar las mejores combinaciones. El análisis de los datos de series temporales sugiere que el uso de la validación cruzada y un período de desfase más prolongado conduce a pronósticos más precisos. El estudio también propone eliminar el ruido de los datos de series temporales como un paso de preprocesamiento y utilizar técnicas de minería con evaluadores de selección de atributos y validación cruzada para modelar los cambios en el nivel del mar en áreas costeras caracterizadas por la no linealidad y los cambios climáticos caóticos.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

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.

Palabras clave

PDF

Resumen visto - 31 veces
PDF descargado - 13

Licencia

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

Content Top

[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.

Imagen de portada

Cómo citar

Descargar cita

Resumen

Descargas

Citas

Palabras clave

Licencia