Abstract
With the continuous improvement of observation technology, the complexity of meteorological data elements has increased sharply, and the volume of the model has expanded, which brings inconvenience to conventional weather forecasting and conventional weather forecasting methods based on traditional statistical forecasting. This paper proposes a LSTM weather forecast method based on Bayesian optimization. Through the constructed sample data, the Bayesian optimization method is used to select the optimal parameters of the LSTM, and then the sample is reconstructed through the optimal LSTM, which has achieved better results in terms of accuracy. This study can explore more reasonable sample construction methods for weather attribute characteristics, and LSTM optimal parameter selection methods, and provide a simple, easy-to-use, high-precision weather prediction method for meteorological experts.
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Acknowledgement
This paper can not be completed without the teacher’s guidance and the support of our school. Thank our school for giving us an opportunity to do this research.
Funding Statment: This work was supported in full by NUIST Students’ Platform for Innovation and Entreprneurship Training Program.
Conflicts of Interest: We have no conflicts of interest to report regarding the present study.
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Wu, J., Wang, D., Huang, Z., Qi, J., Wang, R. (2021). Weather Temperature Prediction Based on LSTM-Bayesian Optimization. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1422. Springer, Cham. https://doi.org/10.1007/978-3-030-78615-1_39
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DOI: https://doi.org/10.1007/978-3-030-78615-1_39
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