Multi-step ahead soil temperature forecasting at different depths based on meteorological data: Integrating resampling algorithms and machine learning models

Khosravi, Khabat; Golkarian, Ali; Barzegar, Rahim; Aalami, Mohammad Taghi; HRDDAM, Salim; Omidvar, Ebrahim; Keesstra, Saskia; LÓPEZ-VICENTE, Manuel

doi:10.1016/j.pedsph.2022.06.056

Cited by 5 publications

(2 citation statements)

References 77 publications

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“…Accurately estimating soil temperature is critical to carry out agricultural planting activities. To a certain extent, the planting time of field crops and greenhouse crops all depends on the optimal soil temperature for seed germination and seedling emergence [34,61]. Different crops demand different temperatures for optimal growth.…”

Section: Discussionmentioning

confidence: 99%

“…Many studies have simulated the predicted temperature of surface or shallow soil, but not many simulations of deep soil temperatures have been carried out. However, setting up deep soil monitoring equipment requires a large amount of labor and material costs [34]. Thus, the simulation of deep soil temperatures provides a critical data source for agricultural and land surface management.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the Potential of Using Machine Learning and the Savitzky–Golay Filter to Estimate the Daily Soil Temperature in Gully Regions of the Chinese Loess Plateau

Deng,

Liu,

Guo

et al. 2024

Agronomy

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Soil temperature directly affects the germination of seeds and the growth of crops. In order to accurately predict soil temperature, this study used RF and MLP to simulate shallow soil temperature, and then the shallow soil temperature with the best simulation effect will be used to predict the deep soil temperature. The models were forced by combinations of environmental factors, including daily air temperature (Tair), water vapor pressure (Pw), net radiation (Rn), and soil moisture (VWC), which were observed in the Hejiashan watershed on the Loess Plateau in China. The results showed that the accuracy of the model for predicting deep soil temperature proposed in this paper is higher than that of directly using environmental factors to predict deep soil temperature. In testing data, the range of MAE was 1.158–1.610 °C, the range of RMSE was 1.449–2.088 °C, the range of R2 was 0.665–0.928, and the range of KGE was 0.708–0.885 at different depths. The study not only provides a critical reference for predicting soil temperature but also helps people to better carry out agricultural production activities.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of the Potential of Using Machine Learning and the Savitzky–Golay Filter to Estimate the Daily Soil Temperature in Gully Regions of the Chinese Loess Plateau

Deng,

Liu,

Guo

et al. 2024

Agronomy

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Hybrid machine learning model for prediction of vertical deflection of composite bridges

Hoang

Manh

Nguyen

et al. 2023

Proceedings of the Institution of Civil Engineers - Bridge Engi

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In the present study, we have developed a novel hybrid Machine Learning (ML) based model namely B-IBk which is a combination of Bagging (B) ensemble and Instance-based K-nearest neighbors (IBk) predictor, for quick and accurate prediction of vertical deflection of steel-concrete composite bridges. In the models’ study, we have used five easily determined input parameters: cross-sectional shape, length of concrete beam (m), number of exploitation years, height of main girder (m), and distance between the main girders (m) to obtain output parameter: maximum vertical deflection (mm). For the development of models, direct measurement data of 80 steel-concrete composite bridges located at different places in Vietnam was collected and used as input and output parameters. Standard statistical evaluation indicators namely Mean Absolute Error (MAE), Correlation Coefficient (R), Root Mean Square Error (RMSE) were used to validate and compare the models’ performance. Results indicated that performance of the novel hybrid model B-IBk is very good (R = 0.908) for the prediction of Y of steel-concrete composite Bridge and better than single IBk model (R = 0.875) on testing dataset. Therefore, the developed novel model B-IBk is a promising tool for the accurate prediction of Y of Steel-Concrete Composite Bridges.

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Enhanced forecasting of multi-step ahead daily soil temperature using advanced hybrid vote algorithm-based tree models

Hatamiafkoueieh,

Heddam,

Khoshtinat

et al. 2023

Journal of Hydroinformatics

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In this study, the vote algorithm used to improve the performances of three machine-learning models including M5Prime (M5P), random forest (RF), and random tree (RT) is developed (i.e. V-M5P, V-RF, and V-RT). Developed models were tested for forecasting soil temperature (TS) at 1, 2, and 3 days ahead at depths of 5 and 50 cm. All models were developed using different climatic variables, including mean, minimum, and maximum air temperatures; sunshine hours; evaporation; and solar radiation, which were evaluated. Correlation coefficients of 0.95 for the V-M5P model, 0.95 for the V-RF model, and 0.91 for the V-RT model were recorded for both 1- and 2-day ahead forecasting at a depth of 5 cm. For 3-day ahead forecasting, V-RF was the superior model with Nash–Sutcliff efficiency (NSE) values of 0.85, compared V-M5P's value of 0.81 and V-RT's value of 0.81. The results at a depth of 5 cm indicate that V-RT was the least effective model. At a depth of 50 cm, forecasted TsS was in good agreement with measurements, and the V-RF was slightly superior. Among the limitations of the current work is that the models were unable to improve their performances by increasing the forecasting horizon.

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Multi-step ahead soil temperature forecasting at different depths based on meteorological data: Integrating resampling algorithms and machine learning models

Cited by 5 publications

References 77 publications

Evaluation of the Potential of Using Machine Learning and the Savitzky–Golay Filter to Estimate the Daily Soil Temperature in Gully Regions of the Chinese Loess Plateau

Evaluation of the Potential of Using Machine Learning and the Savitzky–Golay Filter to Estimate the Daily Soil Temperature in Gully Regions of the Chinese Loess Plateau

Hybrid machine learning model for prediction of vertical deflection of composite bridges

Enhanced forecasting of multi-step ahead daily soil temperature using advanced hybrid vote algorithm-based tree models

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