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...

Full description

Saved in:

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
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

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

Similar Items