Non-Linear Models for Assessing Soil Moisture Estimation
Accurately estimating soil moisture (SM) without direct measurements poses significant challenges due to nonlinear interactions in meteorological variables and the lagged response of SM to precipitation. This study evaluates two approaches: the auto-regressive integrated moving average (ARIMA) model...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Horticulturae |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2311-7524/11/5/492 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849327011424632832 |
|---|---|
| author | Rui Li Susu Wang Han Wu Hao Dong Dezhi Kong Hanxue Li Dorothy S. Zhang Haitao Chen |
| author_facet | Rui Li Susu Wang Han Wu Hao Dong Dezhi Kong Hanxue Li Dorothy S. Zhang Haitao Chen |
| author_sort | Rui Li |
| collection | DOAJ |
| description | Accurately estimating soil moisture (SM) without direct measurements poses significant challenges due to nonlinear interactions in meteorological variables and the lagged response of SM to precipitation. This study evaluates two approaches: the auto-regressive integrated moving average (ARIMA) model for one-day-ahead SM forecasting and a K-means clustering-based multilayer perceptron (K-MLP) for real-time SM estimation at depths of 5 cm, 20 cm, and 50 cm in Changbai Mountain region. Although the K-MLP model outperformed the MLP model, achieving a maximum R<sup>2</sup> of 0.728, its estimation accuracy remains suboptimal. By contrast, the ARIMA model effectively leveraged SM persistence, achieving high accuracy in one-day-ahead forecasting. Specifically, the ARIMA (0, 1, 6), ARIMA (1, 1, 2), and ARIMA (2, 1, 1) models yield R<sup>2</sup> values of 0.9677, 0.9853, and 0.9684 and RMSE values of 0.02 m<sup>3</sup>·m<sup>3</sup>, 0.015 m<sup>3</sup>·m<sup>3</sup>, and 0.006 m<sup>3</sup>·m<sup>3</sup> at depths of 5 cm, 20 cm, and 50 cm, respectively. This study explores ARIMA’s robustness in short-term SM forecasting and its adaptability to dynamic meteorological conditions, offering potential applications in agricultural water management and ecological monitoring. |
| format | Article |
| id | doaj-art-8f6ed6a317da4ee4ac55e21aac5f36f8 |
| institution | Kabale University |
| issn | 2311-7524 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Horticulturae |
| spelling | doaj-art-8f6ed6a317da4ee4ac55e21aac5f36f82025-08-20T03:47:59ZengMDPI AGHorticulturae2311-75242025-04-0111549210.3390/horticulturae11050492Non-Linear Models for Assessing Soil Moisture EstimationRui Li0Susu Wang1Han Wu2Hao Dong3Dezhi Kong4Hanxue Li5Dorothy S. Zhang6Haitao Chen7College of Engineering, Northeast Agricultural University, Harbin 150030, ChinaCollege of Engineering, Northeast Agricultural University, Harbin 150030, ChinaSchool of Computer and Internet of Things Engineering, Chongqing Institute of Engineering, Chongqing 400900, ChinaSchool of Public Administration, Dongbei University of Finance and Economics, Dalian 116025, ChinaCollege of Engineering, Northeast Agricultural University, Harbin 150030, ChinaCollege of Mechanical and Electrical Information, Shangqiu University, Shangqiu 476000, ChinaComputing Technology and Information Systems Department, Guilford College, Greensboro, NC 27410, USACollege of Engineering, Northeast Agricultural University, Harbin 150030, ChinaAccurately estimating soil moisture (SM) without direct measurements poses significant challenges due to nonlinear interactions in meteorological variables and the lagged response of SM to precipitation. This study evaluates two approaches: the auto-regressive integrated moving average (ARIMA) model for one-day-ahead SM forecasting and a K-means clustering-based multilayer perceptron (K-MLP) for real-time SM estimation at depths of 5 cm, 20 cm, and 50 cm in Changbai Mountain region. Although the K-MLP model outperformed the MLP model, achieving a maximum R<sup>2</sup> of 0.728, its estimation accuracy remains suboptimal. By contrast, the ARIMA model effectively leveraged SM persistence, achieving high accuracy in one-day-ahead forecasting. Specifically, the ARIMA (0, 1, 6), ARIMA (1, 1, 2), and ARIMA (2, 1, 1) models yield R<sup>2</sup> values of 0.9677, 0.9853, and 0.9684 and RMSE values of 0.02 m<sup>3</sup>·m<sup>3</sup>, 0.015 m<sup>3</sup>·m<sup>3</sup>, and 0.006 m<sup>3</sup>·m<sup>3</sup> at depths of 5 cm, 20 cm, and 50 cm, respectively. This study explores ARIMA’s robustness in short-term SM forecasting and its adaptability to dynamic meteorological conditions, offering potential applications in agricultural water management and ecological monitoring.https://www.mdpi.com/2311-7524/11/5/492soil moisture estimationauto-regressive integrated moving averagemultilayer perceptronK-means clustering |
| spellingShingle | Rui Li Susu Wang Han Wu Hao Dong Dezhi Kong Hanxue Li Dorothy S. Zhang Haitao Chen Non-Linear Models for Assessing Soil Moisture Estimation Horticulturae soil moisture estimation auto-regressive integrated moving average multilayer perceptron K-means clustering |
| title | Non-Linear Models for Assessing Soil Moisture Estimation |
| title_full | Non-Linear Models for Assessing Soil Moisture Estimation |
| title_fullStr | Non-Linear Models for Assessing Soil Moisture Estimation |
| title_full_unstemmed | Non-Linear Models for Assessing Soil Moisture Estimation |
| title_short | Non-Linear Models for Assessing Soil Moisture Estimation |
| title_sort | non linear models for assessing soil moisture estimation |
| topic | soil moisture estimation auto-regressive integrated moving average multilayer perceptron K-means clustering |
| url | https://www.mdpi.com/2311-7524/11/5/492 |
| work_keys_str_mv | AT ruili nonlinearmodelsforassessingsoilmoistureestimation AT susuwang nonlinearmodelsforassessingsoilmoistureestimation AT hanwu nonlinearmodelsforassessingsoilmoistureestimation AT haodong nonlinearmodelsforassessingsoilmoistureestimation AT dezhikong nonlinearmodelsforassessingsoilmoistureestimation AT hanxueli nonlinearmodelsforassessingsoilmoistureestimation AT dorothyszhang nonlinearmodelsforassessingsoilmoistureestimation AT haitaochen nonlinearmodelsforassessingsoilmoistureestimation |