El Niño Index forecasting using machine learning techniques

El Niño is a large-scale ocean-atmospheric coupling phenomenon in the Pacific. The interaction among marine and atmospheric variables over the tropical Pacific modulate the evolution of El Niño. The latest research shows that machine learning and neural network (NN) have appeared as effective tools to achieve meaningful information from multiple marine and atmospheric parameters. In this paper, we aim to predict the El Niño index more accurately and increase the forecast efficiency of El Niño events. Here, we propose an approach combining a neural network technique with long short-term memory (LSTM) neural network to forecast El Niño phenomenon. The attributes of model are resulted from physical explanation which are tested with the experiments and observations. The neural network represents the connection among multiple variables and machine learning creates models to identify the El Niño events. The preliminary experimental results exhibit that training NN-LSTM model on network metrics time series dataset provides great potential for predicting El Niño phenomenon at lag times of up to more than 6 months.