Predictive Model of the ENSO Phenomenon Based on Regression Trees
Abstract
In this work, the supervised machine learning technique was applied to develop a predictive model of the phase of the El Niño-Southern Oscillation (ENSO) phenomenon. Regression trees were specifically used by means of the Scikit-Learn library of the Python programming language. Data from the period 1950-2022 were used as training and test. The performance of the predictive model was validated using three continuous type error measurement metrics: Mean Absolute Error, Maximum Error and Root Mean Square Root. The results indicate that with a greater number of training data the model improves its performance, with a tendency to decrease the error in forecasts. Which starts for the year 1953 with errors of 0.77, 1.41 and 0.75 for MAE, ME and RMSE respectively, ending for the year 2022 with errors of 0.28, 0.72 and 0.13 for the same metrics. It is concluded that, based on the results, the developed model is consistent and reliable for ENSO phase forecasts in a 12-month window.
References
National oceanic and atmospheric administration (noaa). monthly nino-3.4 index. https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/detrend.nino34.ascii.txt [accessed on January 6, 2023].
Amami, R., Al Saif, S. A., Amami, R., Eleraky, H. A., Melouli, F., and Baazaoui, M. The use of an incremental learning algorithm for diagnosing covid-19 from chest x-ray images. MENDEL Journal 28, 1 (2022), 1–7.
Beaulieu-Jones, B., Finlayson, S. G., Chivers, C., Chen, I., McDermott, M., Kandola, J., Dalca, A. V., Beam, A., Fiterau, M., and Naumann, T. Trends and focus of machine learning applications for health research. JAMA network open 2, 10 (2019), e1914051–e1914051.
Birk, K., Lupo, A. R., Guinan, P., and Barbieri, C. The interannual variability of midwestern temperatures and precipitation as related to the enso and pdo. Atmosfera 23, 2 (2010), 95–128.
Breiman, L. Classification and regression trees. Routledge, 2017.
Cane, M. A. Oceanographic events during el nino. Science 222, 4629 (1983), 1189–1195.
Carrera, E. J. B. Sobre las variaciones climaticas en mexico. Ingenierıa de Recursos Naturales y del Ambiente, 11 (2012), 117–127.
Cerano-Paredes, J., Villanueva-Dıaz, J., Valdez-Cepeda, R. D., Arreola-Avila, J. G., and Constante-Garcıa, V. El nino oscilacion del sur y sus efectos en la precipitacion en la parte alta de la cuenca del r´ıo nazas. Revista Chapingo serie ciencias forestales y del ambiente 17, SPE (2011), 207–215.
Charbuty, B., and Abdulazeez, A. Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends 2, 01 (2021), 20–28.
Conde, C. Mexico y el cambio climatico global. Universidad Nacional Autonoma de Mexico, Direccion General de Divulgacion de . . . , 2006.
Diaz, H. F. El Nino and the Southern Oscillation: multiscale variability and global and regional impacts. Cambridge University Press, 2000.
Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H. The elements of statistical learning: data mining, inference, and prediction, vol. 2. Springer, 2009.
Kousky, V., and Higgins, R. An alert classification system for monitoring and assessing the enso cycle. Weather and Forecasting 22, 2 (2007), 353–371.
Kung, E. C., and Chern, J.-G. Prevailing anomaly patterns of the global sea surface temperatures and tropospheric responses. Atmosfera 8, 3 (1995).
Lupo, A., Kelsey, E., Weitlich, D., Woolard, J., Mokhov, I., Guinan, P. E., and Akyuz, F. Interannual and interdecadal variability in the predominant pacific region sst anomaly patterns and their impact on climate in the mid-mississippi valley region. Atmosfera 20, 2 (2007), 171–196.
McPhaden, M. J. Toga-tao and the 1991-93 el nino-southern oscillation event. Oceanography 6, 2 (1993), 36–44.
McPhaden, M. J. Genesis and evolution of the 1997-98 el ni˜no. Science 283, 5404 (1999), 950– 954.
Minh, H. T., Anh, T. P., et al. A novel lightweight dcnn model for classifying plant diseases on internet of things edge devices. MENDEL Journal 28, 2 (2022), 41–48.
Molina, J. J. C. El fenomeno enso (el nino. oscilacion del sur) en 1997-1998: alteraciones clim´aticas inducidas en el mundo. Nimbus: Revista de climatologia, meteorologia y paisaje, 3 (1999), 37–62.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. Scikit-learn: Machine learning in python. the Journal of machine Learning research 12 (2011), 2825–2830.
Quinlan, J. R. Induction of decision trees. Machine learning 1 (1986), 81–106.
Quinlan, J. R. C4. 5: programs for machine learning. Elsevier, 2014.
Ramırez Solano, A. M., Chamizo Garcia, H. A., and Fallas Sojo, J. C. Fenomeno enos y el dengue, regiones pacifico central y huetar atl´antico, costa rica, 1990 a 2011. Poblacion y Salud en Mesoam´erica 15, 1 (2017), 6–25.
Ritter, W., Guzman, S., Sanchez-Santillan, N., Suarez, J., Corona, C., Munoz, H., Ramos, A., Rodriguez, R., and Perez, T. El clima como sistema complejo adaptativo en coevoluci´on. Ciencia y Mar 6, 17 (2002), 23–25.
Rodrıguez-Moreno, V. M., Ruız-Corral, J. A., Medina-Garcıa, G., Padilla-Ramırez, J. S., and Gunter Kretzschmar, T. Efecto de la condici´on enso en la frecuencia e intensidad de los eventos de lluvia en la penınsula de baja california (1998-2012). Revista mexicana de ciencias agrıcolas 5, SPE10 (2014), 1923–1937.
Sanchez-Santillan, N., Lanza-Espino, G., Garduno, R., and Sanchez-Trejo, R. La influencia antropogenica en el cambio clim´atico bajo la optica de los sistemas complejos. Rev. Iberoam. Ciencias 2, 6 (2015), 69–84.
Valiente, O. M. Evolucion en el estudio del fenomeno enso (el nino-oscilacion del sur): de anomalıa local a la prediccion de variaciones climaticas globales. Investigaciones Geograficas (Esp), 21 (1999), 5–20.
Xiang, B., Zeng, C., Dong, X., and Wang, J. The application of a decision tree and stochastic forest model in summer precipitation prediction in chongqing. Atmosphere 11, 5 (2020), 508.
Xu, Z., Liu, D., and Yan, L. Temperature-based fire frequency analysis using machine learning: A case of changsha, china. Climate Risk Management 31 (2021), 100276.
Zhang, Y., Wallace, J. M., and Battisti, D. S. Enso-like interdecadal variability: 1900–93. Journal of climate 10, 5 (1997), 1004–1020.
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