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El Niño-Southern Oscillation forecasting using complex networks analysis of LSTM neural networks

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Abstract

Arguably, 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.

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Authors and Affiliations

  1. University of Ghana, Legon, Accra, Ghana

    Clifford Broni-Bedaiko, Ferdinand Apietu Katsriku, Jamal-Deen Abdulai & Ebenezer Owusu

  2. Soka University, Hachioji-shi, Tokyo, Japan

    Clifford Broni-Bedaiko, Tatsuo Unemi, Masayasu Atsumi & Norihiko Shinomiya

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  1. Clifford Broni-Bedaiko
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  2. Ferdinand Apietu Katsriku
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  3. Tatsuo Unemi
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  4. Masayasu Atsumi
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  5. Jamal-Deen Abdulai
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  6. Norihiko Shinomiya
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  7. Ebenezer Owusu
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Correspondence to Clifford Broni-Bedaiko.

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Broni-Bedaiko, C., Katsriku, F.A., Unemi, T. et al. El Niño-Southern Oscillation forecasting using complex networks analysis of LSTM neural networks. Artif Life Robotics 24, 445–451 (2019). https://doi.org/10.1007/s10015-019-00540-2

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