scholarly journals Soil Temperature Prediction via Self-Training: Izmir Case

2022 ◽  
pp. 47-62
Author(s):  
Göksu TÜYSÜZOĞLU ◽  
Derya BİRANT ◽  
Volkan KIRANOGLU
Keyword(s):  
Soil Temperature ◽  
Temperature Prediction

Soil Temperature Prediction on Farmland Water Level and Environmental Factors Paddy Based on BP-ANN Model

2012 ◽  
Vol 518-523 ◽  
pp. 4247-4252
Author(s):  
Meng Hua Xiao ◽  
Shuan Gen Yu ◽  
Chun Xiao Fu ◽  
Li Li Kong ◽  
Jun Wang ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Soil Temperature ◽  
Water Level ◽  
Predictive Value ◽  
Total Solar Radiation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Level Control ◽  
Temperature Prediction ◽  
Drought Treatment

The main environmental factors of the influence of soil temperature are air temperature, total solar radiation, effective photosynthetic radiation, wind speed and relative humidity, while the farmland water and submerging duration are the main factors affecting the change of soil temperature, and it exist complex non linear relationship between them. The aim of this study was constructed the soil temperature prediction model on farmland water level and environmental factors and obtained paddy soil temperature predictive value by using the data of existing water level and environmental factors. For the flooding treatment, the minimum and maximum soil temperature appeared at 7:00 and 18:00, for the drought treatment the minimum and maximum soil temperature respectively appeared at 6:00 and 14:00. This study trained artificial neural network based on back propagation algorithm (BP-ANN) through the existing data to predict the four characteristics the soil temperature of different water level control so as to obtain the soil temperature amplitude. Results showed that there was certain deviation between the predictive value and the actual value at the four characteristic moments, relative error were 1.19%, 1.34%, 2.09% and 1.07%, the predicted outcome was satisfactory. It is significant for guiding the rice irrigation and the production of practice facilitated.

A Revised Force–Restore Model for Land Surface Modeling

2004 ◽  
Vol 43 (11) ◽  
pp. 1768-1782 ◽  
Author(s):  
Diandong Ren ◽  
Ming Xue
Keyword(s):  
Soil Temperature ◽  
Skin Temperature ◽  
Land Surface ◽  
Deep Layer ◽  
Additional Term ◽  
Prediction Equation ◽  
Lapse Rate ◽  
Temperature Prediction ◽  
Land Surface Modeling ◽  
Definition Of

Abstract To clarify the definition of the equation for the temperature toward which the soil skin temperature is restored, the prediction equations in the commonly used force–restore model for soil temperature are rederived from the heat conduction equation. The derivation led to a deep-layer temperature, commonly denoted T2, that is defined as the soil temperature at depth πd plus a transient term, where d is the e-folding damping depth of soil temperature diurnal oscillations. The corresponding prediction equation for T2 has the same form as the commonly used one except for an additional term involving the lapse rate of the “seasonal mean” soil temperature and the damping depth d. A term involving the same also appears in the skin temperature prediction equation, which also includes a transient term. In the literature, T2 was initially defined as the short-term (over several days) mean of the skin temperature, but in practice it is often used as the deep-layer temperature. Such inconsistent use can lead to drift in T2 prediction over a several-day period, as is documented in this paper. When T2 is properly defined and initialized, large drift in T2 prediction is avoided and the surface temperature prediction is usually improved. This is confirmed by four sets of experiments, each for a period during each season of 2000, that are initialized using and verified against measurements of the Oklahoma Atmospheric Surface-Layer Instrumentation System (OASIS) project.

scholarly journals An attention-aware LSTM model for soil moisture and soil temperature prediction

Geoderma ◽  
2022 ◽  
Vol 409 ◽  
pp. 115651
Author(s):  
Qingliang Li ◽  
Yuheng Zhu ◽  
Wei Shangguan ◽  
Xuezhi Wang ◽  
Lu Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Temperature Prediction

RETRACTED: Application of adaptive neuro-fuzzy technique to determine the most relevant variables for daily soil temperature prediction at different depths

CATENA ◽  
2016 ◽  
Vol 145 ◽  
pp. 204-213 ◽  
Author(s):  
Kasra Mohammadi ◽  
Shahaboddin Shamshirband ◽  
Amirrudin Kamsin ◽  
Raja Jamilah Raja Yusof
Keyword(s):  
Soil Temperature ◽  
Temperature Prediction ◽  
Neuro Fuzzy ◽  
Relevant Variables ◽  
Different Depths

scholarly journals Knowledge‐guided machine learning for improving daily soil temperature prediction across the United States

2021 ◽  
Author(s):  
Olufemi P. Abimbola ◽  
George E. Meyer ◽  
Aaron R. Mittelstet ◽  
Daran R. Rudnick ◽  
Trenton E. Franz
Keyword(s):  
United States ◽  
Machine Learning ◽  
Soil Temperature ◽  
The United States ◽  
Temperature Prediction
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