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Deep Learning Model for Global Spatio-Temporal Image Prediction

Author

Listed:
  • Dušan P. Nikezić

    (Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia)

  • Uzahir R. Ramadani

    (Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia)

  • Dušan S. Radivojević

    (Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia)

  • Ivan M. Lazović

    (Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia)

  • Nikola S. Mirkov

    (Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia)

Abstract

Mathematical methods are the basis of most models that describe the natural phenomena around us. However, the well-known conventional mathematical models for atmospheric modeling have some limitations. Machine learning with Big Data is also based on mathematics but offers a new approach for modeling. There are two methodologies to develop deep learning models for spatio-temporal image prediction. On these bases, two models were built—ConvLSTM and CNN-LSTM—with two types of predictions, i.e., sequence-to-sequence and sequence-to-one, in order to forecast Aerosol Optical Thickness sequences. The input dataset for training was NASA satellite imagery MODAL2_E_AER_OD from Terra/MODIS satellites, which presents global Aerosol Optical Thickness with an 8 day temporal resolution from 2000 to the present. The obtained results show that the ConvLSTM sequence-to-one model had the lowest RMSE error and the highest Cosine Similarity value. The advantages of the developed DL models are that they can be executed in milliseconds on a PC, can be used for global-scale Earth observations, and can serve as tracers to study how the Earth’s atmosphere moves. The developed models can be used as transfer learning for similar image time-series forecasting models.

Suggested Citation

  • Dušan P. Nikezić & Uzahir R. Ramadani & Dušan S. Radivojević & Ivan M. Lazović & Nikola S. Mirkov, 2022. "Deep Learning Model for Global Spatio-Temporal Image Prediction," Mathematics, MDPI, vol. 10(18), pages 1-15, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3392-:d:918568
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    References listed on IDEAS

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    1. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
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    Cited by:

    1. Dušan S. Radivojević & Ivan M. Lazović & Nikola S. Mirkov & Uzahir R. Ramadani & Dušan P. Nikezić, 2023. "A Comparative Evaluation of Self-Attention Mechanism with ConvLSTM Model for Global Aerosol Time Series Forecasting," Mathematics, MDPI, vol. 11(7), pages 1-13, April.
    2. Devi Munandar & Budi Nurani Ruchjana & Atje Setiawan Abdullah & Hilman Ferdinandus Pardede, 2023. "Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting," Mathematics, MDPI, vol. 11(13), pages 1-25, July.

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