Multi-depth daily soil temperature modeling: meteorological variables or time series?

Ebtehaj, Isa; Bonakdari, Hossein; Samui, Pijush; Gharabaghi, Bahram

doi:10.1007/s00704-022-04314-y

Cited by 4 publications

(2 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The statistical properties are displayed in Table 1 . It is interesting to note that, the authors reviewed various papers and selected the most impactful variables from the literature, emphasizing commonly available ones 7 , 56 , 57 . Soil temperature mirrors air temperature at the surface, but deeper layers are more stable and lag behind in seasonal shifts observed at the top 1 .…”

Section: Methodsmentioning

confidence: 99%

Soil temperature forecasting using a hybrid artificial neural network in Florida subtropical grazinglands agro-ecosystems

Biazar,

Shehadeh,

Ghorbani

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Soil temperature is a key meteorological parameter that plays an important role in determining rates of physical, chemical and biological reactions in the soil. Ground temperature can vary substantially under different land cover types and climatic conditions. Proper prediction of soil temperature is thus essential for the accurate simulation of land surface processes. In this study, two intelligent neural models—artificial neural networks (ANNs) and Sperm Swarm Optimization (SSO) were used for estimating of soil temperatures at four depths (5, 10, 20, 50 cm) using seven-year meteorological data acquired from Archbold Biological Station in South Florida. The results of this study in subtropical grazinglands of Florida showed that the integrated artificial neural network and SSO models (MLP-SSO) were more accurate tools than the original structure of artificial neural network methods for soil temperature forecasting. In conclusion, this study recommends the hybrid MLP-SSO model as a suitable tool for soil temperature prediction at different soil depths.

show abstract

Section: Methodsmentioning

confidence: 99%

Soil temperature forecasting using a hybrid artificial neural network in Florida subtropical grazinglands agro-ecosystems

Biazar,

Shehadeh,

Ghorbani

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Estimating the T S time series by using artificial intelligence (AI) models and exogenous variables is common. For instance, multilayer perceptron (MLP), an artificial neural network (ANN) model and a multivariate linear regression with air temperature (Ta), solar radiation (RS), relative humidity (RH), and precipitation inputs as inputs [15], MLP and an adaptive neuro-fuzzy inference system, genetic programming and an ANN with Ta, RS, RH, wind speed, and dew points inputs [16][17][18], a self-adaptive evolutionary extreme learning machine with Rs, Ta, and pressure [19], an emotional neural network, and a least square support vector machine [20] are some of the AI methods used in this field. Though these modeling methods are powerful in creating estimations, they are prone to model parameter tuning and input selection uncertainties [21,22].…”

Section: Introductionmentioning

confidence: 99%

Multi-Tempo Forecasting of Soil Temperature Data; Application over Quebec, Canada

2023

Self Cite

View full text Add to dashboard Cite

The profound impact of soil temperature (TS) on crucial environmental processes, including water infiltration, subsurface movement, plant growth, and its influence on land–atmosphere dynamics, cannot be undermined. While satellite and land surface model-based data are valuable in data-sparse areas, they necessitate innovative solutions to bridge gaps and overcome temporal delays arising from their dependence on atmospheric and hydro–meteorological factors. This research introduces a viable technique to address the lag in the Famine Early Warning Network Land Data Assimilation System (FLDAS). Notably, this approach exhibits versatility, proving highly effective in analyzing datasets characterized by significant seasonal trends, and its application holds immense value in watershed-scaled hydrological research. Leveraging the enhanced state-space (SS) method for forecasting in the FLDAS, this technique harnesses TS datasets collected over time at various depths (0–10 cm, 10–40 cm, and 40–100 cm), employing a multiplicative SS model for modeling purposes. By employing the 1-step, 6-step, and 12-step-ahead models at different depths and 2 locations in Quebec, Canada, the outcomes showcased a performance with an average coefficient of determination (R2) of 0.88 and root mean squared error (RMSE) of 2.073 °C for the dynamic model, R2 of 0.834 and RMSE of 2.979 °C for the 6-step-ahead model, and R2 of 0.921 and RMSE of 1.865 °C for the 12-step-ahead model. The results revealed that as the prediction horizon expands and the length of the input data increases, the accuracy of predictions progressively improves, indicating that this model becomes increasingly accurate over time.

show abstract

A comparative analysis of deep learning models for soil temperature prediction in cold climates

Imanian,

Mohammadian,

Farhangmehr

et al. 2023

Theor Appl Climatol

View full text Add to dashboard Cite

Multi-depth daily soil temperature modeling: meteorological variables or time series?

Cited by 4 publications

References 70 publications

Soil temperature forecasting using a hybrid artificial neural network in Florida subtropical grazinglands agro-ecosystems

Soil temperature forecasting using a hybrid artificial neural network in Florida subtropical grazinglands agro-ecosystems

Multi-Tempo Forecasting of Soil Temperature Data; Application over Quebec, Canada

A comparative analysis of deep learning models for soil temperature prediction in cold climates

Contact Info

Product

Resources

About