Using singular spectrum analysis and empirical mode decomposition to enhance the accuracy of a machine learning-based soil moisture forecasting algorithm-Reference-Cited by-同舟云学术

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Using singular spectrum analysis and empirical mode decomposition to enhance the accuracy of a machine learning-based soil moisture forecasting algorithm

Published:2024-09 Issue: Volume:224 Page:109200
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Murcia Eduart^ORCID,Guzmán Sandra M.^ORCID

Publisher

Elsevier BV

Reference86 articles.

1. Machine learning for predicting field soil moisture using soil, crop, and nearby weather station data in the red river valley of the north;Acharya;Soil Syst.,2021

2. Sub-monthly prediction of harmful algal blooms based on automated cell imaging;Agarwal;Harmful Algae,2023

3. Optuna: A next-generation hyperparameter optimization framework;Akiba,2019

4. Artificial intelligence modelling integrated with singular spectral analysis and seasonal-trend decomposition using loess approaches for streamflow predictions;Apaydin;J. Hydrol.,2021

5. A machine learning data fusion model for soil moisture retrieval;Batchu,2023

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