센서학회지 (Journal of Sensor Science and Technology)

  • 제32권3호
  • /
  • Pages.164-168
  • /
  • 2023
  • /
  • 1225-5475(pISSN)
  • /
  • 2093-7563(eISSN)
한국센서학회 (The Korean Sensors Society)
권호 표지
DOI QR코드

DOI QR Code

Development of a Weather Prediction Device Using Transformer Models and IoT Techniques

  • 투고 : 2023.04.30
  • 심사 : 2023.05.27
  • 발행 : 2023.05.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Accurate and reliable weather forecasts for temperature, relative humidity, and precipitation using advanced transformer models and IoT are essential in various fields related to global climate change. We propose a novel weather prediction device that integrates state-of-the-art transformer models and IoT techniques to improve prediction accuracy and real-time processing. The proposed system demonstrated high reliability and performance, offering valuable insights for industries and sectors that rely on accurate weather information, including agriculture, transportation, and emergency response planning. The integration of transformer models with the IoT signifies a substantial advancement in weather and climate modeling.

키워드

참고문헌

  1. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning", Nature., Vol. 521, No. 7553, pp. 436-444, 2015.  https://doi.org/10.1038/nature14539
  2. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, and I. Polosukhin, "Attention is all you need", Adv. Neural Inf. Process. Syst., pp. 5998-6008, 2017. 
  3. H. Wang, H. Guan, and J. Chen, "A transformer-based approach for weather prediction", Proc. of the 2020 Int. Conf. Artif. Intell. Comp. Sci., pp. 171-176, 2020. 
  4. J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding", arXiv, pp.1810.04805(1)-1810.04805(16), 2018. 
  5. S. Bai, J. Z. Kolter, and V. Koltun, "An empirical evaluation of generic convolutional and recurrent networks for sequence modeling", arXiv, pp. 1803.01271(1)-1803.01271(14), 2018. 
  6. A. Katharopoulos, A. Vyas, N. Pappas, F. Fleuret, "Transformers are RNNs: Fast autoregressive transformers with linear attention", Proc. of the 37th Inter. Conf. Mach. Learn., pp. 5156-5165, 2020. 
  7. M. A. Rodriguez, G. Mateos, and S. Alonso, "Weather forecasting with transformer models: a comparative study", Environ. Model. Softw., Vol. 141, pp. 105057, 2021. 
  8. Y. J. Ma and M. Y, Zhai, "A Dual -Step Integrated Machine Learning Model for 24h-Ahead Wind Energy Generation Prediction Based on Actual Measurement Data and Environmental Factors", MDPI(Sens.)., Vol. 9, No. 10, pp. 1-21, 2019. https://doi.org/10.3390/app9102125