IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i18p3392-d918568.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/18/3392/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/18/3392/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liao, Zhouyi & Coimbra, Carlos F.M., 2024. "Hybrid solar irradiance nowcasting and forecasting with the SCOPE method and convolutional neural networks," Renewable Energy, Elsevier, vol. 232(C).
    2. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    3. Xue-Bo Jin & Wen-Tao Gong & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su, 2022. "PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data," Mathematics, MDPI, vol. 10(4), pages 1-17, February.
    4. Li, Xuan & Zhang, Wei, 2022. "Physics-informed deep learning model in wind turbine response prediction," Renewable Energy, Elsevier, vol. 185(C), pages 932-944.
    5. Belqasem Aljafari & Siva Rama Krishna Madeti & Priya Ranjan Satpathy & Sudhakar Babu Thanikanti & Bamidele Victor Ayodele, 2022. "Automatic Monitoring System for Online Module-Level Fault Detection in Grid-Tied Photovoltaic Plants," Energies, MDPI, vol. 15(20), pages 1-28, October.
    6. Robert Basmadjian & Amirhossein Shaafieyoun, 2023. "Assessing ARIMA-Based Forecasts for the Percentage of Renewables in Germany: Insights and Lessons for the Future," Energies, MDPI, vol. 16(16), pages 1-19, August.
    7. Ding, Jiaqi & Zhao, Pu & Liu, Changjun & Wang, Xiaofang & Xie, Rong & Liu, Haitao, 2024. "From irregular to continuous: The deep Koopman model for time series forecasting of energy equipment," Applied Energy, Elsevier, vol. 364(C).
    8. Xu, Shaozhen & Liu, Jun & Huang, Xiaoqiao & Li, Chengli & Chen, Zaiqing & Tai, Yonghang, 2024. "Minutely multi-step irradiance forecasting based on all-sky images using LSTM-InformerStack hybrid model with dual feature enhancement," Renewable Energy, Elsevier, vol. 224(C).
    9. Liu, Zhi-Feng & Chen, Xiao-Rui & Huang, Ya-He & Luo, Xing-Fu & Zhang, Shu-Rui & You, Guo-Dong & Qiang, Xiao-Yong & Kang, Qing, 2024. "A novel bimodal feature fusion network-based deep learning model with intelligent fusion gate mechanism for short-term photovoltaic power point-interval forecasting," Energy, Elsevier, vol. 303(C).
    10. Ruizhi Ji, 2024. "Research on Factors Influencing Global Carbon Emissions and Forecasting Models," Sustainability, MDPI, vol. 16(23), pages 1-17, December.
    11. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    12. Adela Bâra & Simona‐Vasilica Oprea, 2024. "Embedding the weather prediction errors (WPE) into the photovoltaic (PV) forecasting method using deep learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1173-1198, August.
    13. Wang, Jianzhou & Yu, Yue & Zeng, Bo & Lu, Haiyan, 2024. "Hybrid ultra-short-term PV power forecasting system for deterministic forecasting and uncertainty analysis," Energy, Elsevier, vol. 288(C).
    14. Liu, Xingdou & Zhang, Li & Wang, Jiangong & Zhou, Yue & Gan, Wei, 2023. "A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data," Renewable Energy, Elsevier, vol. 211(C), pages 948-963.
    15. Neethu Elizabeth Michael & Manohar Mishra & Shazia Hasan & Ahmed Al-Durra, 2022. "Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique," Energies, MDPI, vol. 15(6), pages 1-20, March.
    16. Negri, Simone & Giani, Federico & Blasuttigh, Nicola & Massi Pavan, Alessandro & Mellit, Adel & Tironi, Enrico, 2022. "Combined model predictive control and ANN-based forecasters for jointly acting renewable self-consumers: An environmental and economical evaluation," Renewable Energy, Elsevier, vol. 198(C), pages 440-454.
    17. Mirza, Adeel Feroz & Mansoor, Majad & Usman, Muhammad & Ling, Qiang, 2023. "A comprehensive approach for PV wind forecasting by using a hyperparameter tuned GCVCNN-MRNN deep learning model," Energy, Elsevier, vol. 283(C).
    18. Huang, Songtao & Zhou, Qingguo & Shen, Jun & Zhou, Heng & Yong, Binbin, 2024. "Multistage spatio-temporal attention network based on NODE for short-term PV power forecasting," Energy, Elsevier, vol. 290(C).
    19. Hu, Yusha & Man, Yi, 2023. "Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    20. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2022. "Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3392-:d:918568. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.