Version 1
: Received: 29 August 2023 / Approved: 31 August 2023 / Online: 5 September 2023 (03:03:04 CEST)
How to cite:
Yibo, Z.; Chengcheng, W.; Pengcheng, W.; Lu, Z.; Qingbo, Y.; Hui, X.; Maofa, W. Improving Precipitation Forecasting in Jilin Province Using Deep Learning and Gaussian Noise. Preprints2023, 2023090156. https://doi.org/10.20944/preprints202309.0156.v1
Yibo, Z.; Chengcheng, W.; Pengcheng, W.; Lu, Z.; Qingbo, Y.; Hui, X.; Maofa, W. Improving Precipitation Forecasting in Jilin Province Using Deep Learning and Gaussian Noise. Preprints 2023, 2023090156. https://doi.org/10.20944/preprints202309.0156.v1
Yibo, Z.; Chengcheng, W.; Pengcheng, W.; Lu, Z.; Qingbo, Y.; Hui, X.; Maofa, W. Improving Precipitation Forecasting in Jilin Province Using Deep Learning and Gaussian Noise. Preprints2023, 2023090156. https://doi.org/10.20944/preprints202309.0156.v1
APA Style
Yibo, Z., Chengcheng, W., Pengcheng, W., Lu, Z., Qingbo, Y., Hui, X., & Maofa, W. (2023). Improving Precipitation Forecasting in Jilin Province Using Deep Learning and Gaussian Noise. Preprints. https://doi.org/10.20944/preprints202309.0156.v1
Chicago/Turabian Style
Yibo, Z., Xu Hui and Wang Maofa. 2023 "Improving Precipitation Forecasting in Jilin Province Using Deep Learning and Gaussian Noise" Preprints. https://doi.org/10.20944/preprints202309.0156.v1
Abstract
This paper explores the use of different deep learning models for predicting precipitation in 56 meteorological stations in Jilin Province, China. The models used include Stacked-LSTM, Transformer, and SVR, and Gaussian noise is added to the data to improve their robustness. Results show that the Stacked-LSTM model performs the best, achieving high prediction accuracy and stability. The study also conducts variable attribution analysis using LightGBM and finds that temperature, dew point, precipitation in previous days, and air pressure are the most important factors affecting precipitation prediction, which is consistent with traditional meteorological theory. The paper provides detailed information on the data processing, model training, and parameter settings, which can serve as a reference for future precipitation prediction tasks. The findings suggest that adding Gaussian noise to the dataset can improve the model's generalization ability, especially for predicting days with zero precipitation. Overall, this study provides useful insights into the application of deep learning models in precipitation prediction and can contribute to the development of meteorological forecasting and applications.
Keywords
deep learning; feature attribution; gaussian noise; LSTM; precipitation prediction; RMSE
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
