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Short- and long-term weather prediction based on a hybrid of CEEMDAN, LMD, and ANN

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  • Samuel Asante Gyamerah
  • Victor Owusu

Abstract

Agriculture is one of the major economic sectors in Africa, and it predominantly depends on the climate. However, extreme climate changes do have a negative impact on agricultural production. The damage resulting from extreme climate change can be mitigated if farmers have access to accurate weather forecasts, which can enable them to make the necessary adjustments to their farming practices. To improve weather prediction amidst extreme climate change, we propose a novel prediction model based on a hybrid of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), local mean decomposition (LMD), and artificial neural networks (NN). A detailed comparison of the performance metrics for the short- and long-term prediction results with other prediction models reveals that the three-phase hybrid CEEMDAN-LMD-NN model is optimal in terms of the evaluation metrics used. The study’s findings demonstrate the efficiency of the three-phase hybrid CEEMDAN-LMD-NN prediction model in decision-system design, particularly for large-scale commercial farmers, small-holder farmers, and the agricultural index insurance industry that require reliable forecasts generated at multi-step horizons.

Suggested Citation

  • Samuel Asante Gyamerah & Victor Owusu, 2024. "Short- and long-term weather prediction based on a hybrid of CEEMDAN, LMD, and ANN," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-23, July.
    • Handle: RePEc:plo:pone00:0304754
    DOI: 10.1371/journal.pone.0304754
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    References listed on IDEAS

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    1. Samuel Asante Gyamerah & Ning Cai, 2021. "Two-Stage Hybrid Machine Learning Model for High-Frequency Intraday Bitcoin Price Prediction Based on Technical Indicators, Variational Mode Decomposition, and Support Vector Regression," Complexity, Hindawi, vol. 2021, pages 1-15, December.
    2. Paniagua-Tineo, A. & Salcedo-Sanz, S. & Casanova-Mateo, C. & Ortiz-García, E.G. & Cony, M.A. & Hernández-Martín, E., 2011. "Prediction of daily maximum temperature using a support vector regression algorithm," Renewable Energy, Elsevier, vol. 36(11), pages 3054-3060.
    3. Yongmei Fang & Bo Guan & Shangjuan Wu & Saeed Heravi, 2020. "Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 877-886, September.
    4. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
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