Prediction of the ENSO using Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) supervised learning from other ocean indices

Abstract El Niño-Southern Oscillation (ENSO) is a cyclical global climate phenomenon that frequently results in global climatic anomalies and has significant effects on the economy and society. Thus, forecasting and studying ENSO events is crucial to comprehending and resolving concerns related to global climate change. It is highly significant both practically and scientifically. Numerical modeling techniques and conventional statistical analysis were the primary tools employed in earlier ENSO research. To increase the prediction accuracy of ENSO, this study investigates the application of deep learning. The meteorological and marine time data were processed using recurrent neural networks (RNN) with long- and short-term memory (LSTM). The work takes into account LSTM for predicting multifactor-related ENSO episodes and employs several climate indices as input characteristics. The findings demonstrate the effectiveness of LSTM in predicting ENSO episodes, as well as its potential scientific and practical applications. Over 100 epochs, the LSTM model showed consistent improvement with declining training and validation loss. Its mean squared error (MSE) for training was 0.0954 and 0.0862 for testing, indicating strong generalization. Mean absolute error (MAE) remained stable at 0.2255 for training and 0.2198 for testing, affirming its robustness. Visual analysis revealed close alignment between predicted and actual MEI values, highlighting its ability to capture ENSO dynamics' complexities.