Broni-Bedaiko, C.Katsriku, F.A.Unemi, T.Atsumi, M.Abdulai, J-D.Shinomiya, N.Owusu, E.2019-12-132019-12-132019-06-04Broni-Bedaiko, C., Katsriku, F.A., Unemi, T. et al. Artif Life Robotics (2019) 24: 445. https://doi.org/10.1007/s10015-019-00540-2https://doi.org/10.1007/s10015-019-00540-2http://ugspace.ug.edu.gh/handle/123456789/34173Research ArticleArguably, El Niño-Southern Oscillation (ENSO) is the most influential climatological phenomenon that has been intensively researched during the past years. Currently, the scientific community knows much about the underlying processes of ENSO phenomenon, however, its predictability for longer horizons, which is very important for human society and the natural environment is still a challenge in the scientific community. Here we show an approach based on using various complex networks metrics extracted from climate networks with long short-term memory neural network to forecast ENSO phenomenon. The results suggest that the 12-network metrics extracted as predictors have predictive power and the potential for forecasting ENSO phenomenon longer multiple steps ahead.enLSTM neural networksComplex networksENSO forecastingTime series forecastingArticle