Resumen
El Niño Southern Oscillation is one of the significant phenomena that drives global climate variability, showing a relationship with extreme events. Reliable forecasting of ENSO phases can minimize the risks in many critical areas, including water supply, food security, health, and public safety on a global scale. This study develops an ENSO forecasting model using the dynamic evolving neural fuzzy inference system (DENFIS), an artificial intelligence-based data-driven algorithm. To forecast ENSO phases for 1, 2, and 3 months ahead, 42 years (1979?2021) of monthly data of 25 oceanic and continental climatic variables and ENSO-characterizing indices are used. The dataset includes 12 El Niño and 14 La Niña events, of which the latest 2 El Niño and 4 La Niña events are reserved for testing while the remaining data are used for training the model. The potential input variables to the model are short-listed using a cross-correlation analysis. Then a systematic input selection procedure is conducted to identify the best input combinations for the model. The results of this study show that the best performing combination of such climate variables could achieve up to 78.57% accuracy in predicting short-term ENSO phases (up to 3 months ahead). Heat content at 0 to 300 m of central equatorial Pacific shows promising performance in forecasting ENSO phases. Moreover, DENFIS was found to be a reliable tool for forecasting ENSO events using multiple oceanic and continental climate variables.