Resumo

RICARDO, Llugsi,; ROBIN, Álvarez,; PABLO, Lupera,  e  FONTAINE, Allyx,. Analysis of the Relationship between El Niño, CO2 and CH4 Using Neural Networks. Rev Politéc. (Quito) [online]. 2025, vol.56, n.2, pp.119-132. ISSN 2477-8990.  https://doi.org/10.33333/rp.vol56n2.10.

This paper takes a fresh look at the influence of CO2 and CH4 for the identification of “El Niño” episodes. We first introduce the statistical methodology for early detection based on the analysis of the historical information Temperature Anomaly produced in the Oceans by the “El Niño” episode. Second, we introduce a statistical analysis to study a possible relation between CO2 and CH4 with the Temperature Anomaly. We apply three Neural Network models and we analyze the correlation between the obtained series and the real one as well as the respective error metrics. The outcome shows that a Convolutional Encoder Decoder model is the most suitable structure to carry out this purpose because it shows a correlation coefficient of 0.991 and error metrics of MSE (Mean Squared Error) = 0.096, RMSE (Root Mean Squared Error) = 0.309, MAE (Mean Absolute Error) = 0.249. However, the amount of historical information on CH4 becomes a limitation to detect a further relationship.

Palavras-chave : Neural Networks; LSTM; Convolutional Encoder-Decoder; El Niño; La Niña; ENSO.

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