Optimizing Precipitation Forecasting and Agricultural Water Resource Allocation Using the Gaussian-Stacked-LSTM Model

被引:0
|
作者
Wang, Maofa [1 ,2 ]
Yan, Bingcheng [1 ]
Zhang, Yibo [3 ]
Zhang, Lu [1 ]
Wang, Pengcheng [1 ]
Huang, Jingjing [2 ]
Shan, Weifeng [4 ]
Liu, Haijun [4 ]
Wang, Chengcheng [3 ]
Wen, Yimin [1 ]
机构
[1] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin 541004, Peoples R China
[2] Beijing Informat Sci & Technol Univ, Sch Appl Sci, Beijing 100192, Peoples R China
[3] Jilin Prov Meteorol Informat & Network Ctr, Changchun 130062, Peoples R China
[4] Inst Disaster Prevent, Sch Emergency Management, Langfang 065201, Peoples R China
基金
中国国家自然科学基金;
关键词
deep learning; feature attribution; Gaussian noise; LSTM; precipitation prediction; RMSE; JILIN PROVINCE;
D O I
10.3390/atmos15111308
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Our study investigates the use of machine learning models for daily precipitation prediction using data from 56 meteorological stations in Jilin Province, China. We evaluate Stacked Long Short-Term Memory (LSTM), Transformer, and Support Vector Regression (SVR) models, with Stacked-LSTM showing the best performance in terms of accuracy and stability, as measured by the Root Mean Square Error (RMSE). To improve robustness, Gaussian noise was introduced, particularly enhancing predictions for zero-precipitation days. Key predictors identified through variable attribution analysis include temperature, dew point, prior precipitation, and air pressure. Additionally, we demonstrate the practical benefits of precipitation forecasts in optimizing water resource allocation. A prediction-based strategy outperforms equal distribution in managing resources efficiently, as shown in a case study using 2022 Beidahu data. Overall, our research advances precipitation forecasting through deep learning and offers valuable insights for water resource management.
引用
收藏
页数:21
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