Short-term soil moisture content forecasting with a hybrid informer model

Long Wang; Shihan Yao; Chao Huang

doi:10.3389/fsufs.2025.1636499

Short-term soil moisture content forecasting with a hybrid informer model

Long Wang^*
, Shihan Yao
, Chao Huang

^*Corresponding author for this work

Department of Data Science

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

2 Citations (Scopus)

3 Downloads (CityUHK Scholars)

Abstract

This study proposes a novel time-series forecasting approach that integrates the Informer model with the RAO - 1 optimization algorithm for soil water content (SWC) prediction. The method innovatively combines Informer's long-range dependency modeling with RAO-1's efficient hyperparameter optimization to enhance forecasting accuracy. Comparative experiments were conducted using Random Forest, Support Vector Regression, Long Short-Term Memory and Transformer as baseline models on SWC datasets from the Beijing region. The RAO-1-optimized Informer consistently outperforms these baselines in both deterministic and probabilistic forecasting tasks, while also achieving superior computational efficiency. These results highlight the robustness of the proposed method and its potential to support sustainable agricultural water management through accurate SWC prediction.

© 2025 Wang, Yao and Huang.

Original language	English
Article number	1636499
Number of pages	22
Journal	Frontiers in Sustainable Food Systems
Volume	9
Online published	4 Sept 2025
DOIs	https://doi.org/10.3389/fsufs.2025.1636499
Publication status	Published - 2025

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported in part by the Beijing Natural Science Foundation under Grant 4232040, in part by the National Nature Science Foundation of China under Grants 62202044 and 62372039, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515240044, and in part by the Fundamental Research Funds for the Central Universities under Grant FRF-BRA-25-012.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger
SDG 6 Clean Water and Sanitation
SDG 8 Decent Work and Economic Growth
SDG 12 Responsible Consumption and Production
SDG 15 Life on Land

Research Keywords

soil moisture content
RAO-1 algorithm
informer model
time-series forecasting
hyperparameter optimization
deep learning

Publisher's Copyright Statement

This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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10.3389/fsufs.2025.1636499

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title = "Short-term soil moisture content forecasting with a hybrid informer model",

abstract = "This study proposes a novel time-series forecasting approach that integrates the Informer model with the RAO - 1 optimization algorithm for soil water content (SWC) prediction. The method innovatively combines Informer's long-range dependency modeling with RAO-1's efficient hyperparameter optimization to enhance forecasting accuracy. Comparative experiments were conducted using Random Forest, Support Vector Regression, Long Short-Term Memory and Transformer as baseline models on SWC datasets from the Beijing region. The RAO-1-optimized Informer consistently outperforms these baselines in both deterministic and probabilistic forecasting tasks, while also achieving superior computational efficiency. These results highlight the robustness of the proposed method and its potential to support sustainable agricultural water management through accurate SWC prediction. {\textcopyright} 2025 Wang, Yao and Huang.",

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T1 - Short-term soil moisture content forecasting with a hybrid informer model

AU - Wang, Long

AU - Yao, Shihan

AU - Huang, Chao

PY - 2025

Y1 - 2025

N2 - This study proposes a novel time-series forecasting approach that integrates the Informer model with the RAO - 1 optimization algorithm for soil water content (SWC) prediction. The method innovatively combines Informer's long-range dependency modeling with RAO-1's efficient hyperparameter optimization to enhance forecasting accuracy. Comparative experiments were conducted using Random Forest, Support Vector Regression, Long Short-Term Memory and Transformer as baseline models on SWC datasets from the Beijing region. The RAO-1-optimized Informer consistently outperforms these baselines in both deterministic and probabilistic forecasting tasks, while also achieving superior computational efficiency. These results highlight the robustness of the proposed method and its potential to support sustainable agricultural water management through accurate SWC prediction. © 2025 Wang, Yao and Huang.

AB - This study proposes a novel time-series forecasting approach that integrates the Informer model with the RAO - 1 optimization algorithm for soil water content (SWC) prediction. The method innovatively combines Informer's long-range dependency modeling with RAO-1's efficient hyperparameter optimization to enhance forecasting accuracy. Comparative experiments were conducted using Random Forest, Support Vector Regression, Long Short-Term Memory and Transformer as baseline models on SWC datasets from the Beijing region. The RAO-1-optimized Informer consistently outperforms these baselines in both deterministic and probabilistic forecasting tasks, while also achieving superior computational efficiency. These results highlight the robustness of the proposed method and its potential to support sustainable agricultural water management through accurate SWC prediction. © 2025 Wang, Yao and Huang.

KW - soil moisture content

KW - RAO-1 algorithm

KW - informer model

KW - time-series forecasting

KW - hyperparameter optimization

KW - deep learning

UR - https://www.webofscience.com/wos/woscc/full-record/WOS:001573626200001

U2 - 10.3389/fsufs.2025.1636499

DO - 10.3389/fsufs.2025.1636499

M3 - RGC 21 - Publication in refereed journal

SN - 2571-581X

VL - 9

JO - Frontiers in Sustainable Food Systems

JF - Frontiers in Sustainable Food Systems

M1 - 1636499

ER -

Short-term soil moisture content forecasting with a hybrid informer model

Abstract

Funding

UN SDGs

Research Keywords

Publisher's Copyright Statement

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