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Optimization Hybrid of Multiple-Lag LSTM Networks for Meteorological Prediction

Author

Listed:
  • Lin Zhu

    (School of Mathematics, Shandong University, Jinan 250100, China)

  • Zhihua Zhang

    (School of Mathematics, Shandong University, Jinan 250100, China)

  • M. James C. Crabbe

    (Wolfson College, Oxford University, Oxford OX2 6UD, UK)

  • Lipon Chandra Das

    (School of Mathematics, Shandong University, Jinan 250100, China
    Department of Mathematics, University of Chittagong, Chittagong 4331, Bangladesh)

Abstract

Residences in poor regions always depend on rain-fed agriculture, so they urgently need suitable tools to make accurate meteorological predictions. Unfortunately, meteorological observations in these regions are usually sparse and irregularly distributed. Conventional LSTM networks only handle temporal sequences and cannot utilize the links of meteorological variables among stations. GCN-LSTM networks only capture local spatial structures through the simple structures of fixed adjacency matrices, and the CNN-LSTM can only mine gridded meteorological observations for further predictions. In this study, we propose an optimization hybrid of multiple-lag LSTM networks for meteorological predictions. Our model can make full use of observed data at partner stations under different time-lag windows and strong links among the local observations of meteorological variables to produce future predictions. Numerical experiments on the meteorological predictions of Bangladesh demonstrate that our networks are superior to the classic LSTM and its variants GCN-LSTM and CNN-LSTM, as well as the SVM and DT.

Suggested Citation

  • Lin Zhu & Zhihua Zhang & M. James C. Crabbe & Lipon Chandra Das, 2023. "Optimization Hybrid of Multiple-Lag LSTM Networks for Meteorological Prediction," Mathematics, MDPI, vol. 11(22), pages 1-18, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:22:p:4603-:d:1277716
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    References listed on IDEAS

    as
    1. Peter Bauer & Alan Thorpe & Gilbert Brunet, 2015. "The quiet revolution of numerical weather prediction," Nature, Nature, vol. 525(7567), pages 47-55, September.
    2. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
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