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Research on Visibility Forecast Based on LSTM Neural Network

  • Yuliang DaiEmail author
  • Zhenyu Lu
  • Hengde Zhang
  • Tianming Zhan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 550)

Abstract

For series problems in the meteorological field, the long-short-term memory neural network (LSTM) model is applied to the visibility forecast in the Beijing, Tianjin and Hebei region. First of all, the historical meteorological data during the months (Oct.-to-Dec. and Jan.-to-Feb.) of years 2015–2016 in the Beijing, Tianjin and Hebei region is selected as a dataset. Then, the Pearson Correlation Coefficient method is applied to select meteorological factors that have main influence on visibility to construct the training set, and adjust the network model parameters to train the neural network, and establish the input meteorological factors and the visibility of the output. Finally, European Centre for Medium-Range Weather Forecasts (ECMWF) data of the Beijing, Tianjin and Hebei region from October to December in 2017 is used to test the forecast effect of the LSTM model, and compared with the prediction results of the BP neural network. The result shows the visibility forecast based on the LSTM model is significantly better than BP neural network. The TS score in 0–1 km is 0.22, and its error is 0.34 km. The TS score in 1–10 km is 0.51, and its error is 2.18 km. The TS score above 10 km is 0.38, and its error is 6.07 km

Keywords

Visibility forecast Neural network LSTM 

Notes

Acknowledgments

This work has been supported in part by the National Natural Science Foundation of China (Grant No. 61773220), the National Key Research Program of China (Grant No. 2016YFC0203301), the Nature Science Foundation of Jiangsu Province under Grant (No. BK20150523).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yuliang Dai
    • 1
    Email author
  • Zhenyu Lu
    • 1
    • 2
  • Hengde Zhang
    • 3
  • Tianming Zhan
    • 4
  1. 1.School of Electronic and Information EngineeringNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and EquipmentNanjingChina
  3. 3.National Meteorological CenterBeijingChina
  4. 4.Nanjing Audit UniversityNanjingChina

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