Improving Precipitation Forecasting in Jilin Province Using Deep Learning and Gaussian Noise

Yibo Zhang, Wang Chengcheng, Wang Pengcheng, Zhang Lu, Yu Qingbo, Hui Xu, Maofa Wang

Preprints.org · 05 September 2023

In one sentence. Stacked-LSTM beats Transformer and SVR for precipitation at 56 Jilin stations; Gaussian noise augmentation improves robustness and zero-rain prediction.

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.

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