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

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

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...

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Bibliographic Details
Main Authors: Maofa Wang, Bingcheng Yan, Yibo Zhang, Lu Zhang, Pengcheng Wang, Jingjing Huang, Weifeng Shan, Haijun Liu, Chengcheng Wang, Yimin Wen
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Atmosphere
Subjects:
deep learning
feature attribution
Gaussian noise
LSTM
precipitation prediction
RMSE
Online Access:https://www.mdpi.com/2073-4433/15/11/1308
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