A Convolutional Neural Network Model for Soil Temperature Prediction under Ordinary and Hot Weather Conditions: Comparison with a Multilayer Perceptron Model

Farhangmehr, Vahid; Cobo, Juan Hiedra; Mohammadian, Abdolmajid; Payeur, Pierre; Shirkhani, Hamidreza; Imanian, Hanifeh

doi:10.3390/su15107897

Cited by 10 publications

(4 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where z represents the estimated variable, 𝜆 𝑖 the weight or significance of the quantity related to ith sample, and 𝑧 𝑣 𝑖 is the known parameter of the ith sample [7]. Various geostatistical methods differ in determining coefficients of known points.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, it seems necessary to have the technology to eliminate deficiencies of meteorological stations in calculating temperature in sampling intervals and in impassable places where meteorological stations cannot be constructed [6]. Exact spatial estimation of air temperature at ungauged points and impassable highlands is a key prerequisite for agricultural planning and water resource management by different methods such as empirical, semi-empirical, and intelligent models [7]. Remote sensing has high potential in large-scale air temperature estimation using surface temperature images [8].…”

mentioning

confidence: 99%

See 1 more Smart Citation

A Method for Correction of Tropical Rainfall Measuring Mission Satellite Temperature Network in Mazandaran Province

Nadi,

Yousefi Kebriya

2024

IJEE

View full text Add to dashboard Cite

Accurate spatial estimation of temperature is very important in meteorological research. This study investigated the efficiency of temperature products of the Tropical Rainfall Measuring Mission (TRMM) satellite in estimating temperature in Mazandaran Province, and its accuracy were compared with inverse distance weighting and Co-Kriging interpolation methods. Finally, a new method was proposed to improve the accuracy of temperature estimation by combining the TRMM temperature products and terrains. Data recorded at 25 meteorological stations and 26 monthly and annual TRMM satellite images in 2012 and 2013 were used. The results showed a significant correlation between temperature data and satellite products, latitude, and altitude in significance level of 95%. Analyzing error indices showed that TRMM products have underestimation error that this bias error contributed to about 60% of error in these satellite images. Despite the larger error of TRMM products than interpolation methods, the regression analysis results demonstrated the superiority of satellite temperature products over interpolation methods. Furthermore, higher correlation of observed and estimated data showing that satellite products give a better understanding of cold and hot points of the study area despite its underestimation error. Combining satellite temperature products with influential covariates of altitude and latitude in the regression equation reduced the temperature estimation error of the TRMM products by 80%. The estimation precision increased over 70% compared to other temperature interpolation methods. Analyzing isotherm maps indicate the higher temperature of eastern coasts than western coasts. Moreover, evaluating different temperature estimation methods showed the higher precision of the methods that involved covariates than other methods.

show abstract

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

A Method for Correction of Tropical Rainfall Measuring Mission Satellite Temperature Network in Mazandaran Province

Nadi,

Yousefi Kebriya

2024

IJEE

View full text Add to dashboard Cite

show abstract

“…Thus, ref. [39] described a one-dimensional CNN prediction model to demonstrate that the air temperature and surface thermal radiation directly impact the soil temperature prediction model, affecting global warming.…”

Section: Related Workmentioning

confidence: 99%

An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model

Vázquez-Ramírez,

Torres-Ruiz,

Quintero

et al. 2023

Mathematics

View full text Add to dashboard Cite

Several Sun models suggest a radioactive balance, where the concentration of greenhouse gases and the albedo effect are related to the Earth’s surface temperature. There is a considerable increment in greenhouse gases due to anthropogenic activities. Climate change correlates with this alteration in the atmosphere and an increase in surface temperature. Efficient forecasting of climate change and its impacts could be helpful to respond to the threat of c.c. and develop sustainably. Many studies have predicted temperature changes in the coming years. The global community has to create a model that can realize good predictions to ensure the best way to deal with this warming. Thus, we propose a finite-time thermodynamic (FTT) approach in the current work. FTT can solve problems such as the faint young Sun paradox. In addition, we use different machine learning models to evaluate our method and compare the experimental prediction and results.

show abstract

“…Due to the heat generated by degradation in the landfill, the temperature inside the landfill varies dynamically over time and can reach 65 • C [21,22]. In agriculture, the temperature of soil and air changes with the change of temperature day and night [23]. Currently, few published studies consider the impact of temperature on the permeability of unsaturated soil.…”

Section: Introductionmentioning

confidence: 99%

A Temperature-Controlled Apparatus for Gas Permeability under Low Gas Pressure

Wu,

Huang,

Liu

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

The measurement of soil gas permeability is influenced by the temperature and pressure fluctuation in the low gas pressure region. In order to investigate these influences, a soil temperature-controlled apparatus connected to a low-gas-pressure supply equipment is proposed in this study. The low constant gas pressure is supplied by two Mariotte bottles, by which the airflow rate is measured. Meanwhile, the soil specimen is controlled by a temperature-controlled apparatus. During the test, the negative pore water pressure and volume change of the soil specimen are measured. Through the temperature-controlled apparatus, it is observed that as the temperature increases from 25 °C to 60 °C, there is a corresponding increase in soil sample porosity by 5.4%, while the negative pressure of pore water decreases by 11.1%. This can be attributed to the reduction in the surface tension of contractile skin caused by elevated temperatures. Furthermore, due to variations in gas viscosity with temperature, there was a significant decrease in the gas flow rate by 50.5%. And, the relationship between permeability and volumetric gas content at different temperatures in low-pressure regions well confirms the existing power-law model. In addition, the existence of a temperature-independent critical negative pore water pressure is observed, beyond which the intrinsic permeability remains constant. At 36 kPa of negative pore water pressure, the intrinsic permeability at 60 °C exhibits an 81.8% reduction compared to that at 25 °C. This decline in intrinsic permeability can be attributed to a diminished pore connectivity, resulting from elevated temperatures.

show abstract

A Convolutional Neural Network Model for Soil Temperature Prediction under Ordinary and Hot Weather Conditions: Comparison with a Multilayer Perceptron Model

Cited by 10 publications

References 41 publications

A Method for Correction of Tropical Rainfall Measuring Mission Satellite Temperature Network in Mazandaran Province

A Method for Correction of Tropical Rainfall Measuring Mission Satellite Temperature Network in Mazandaran Province

An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model

A Temperature-Controlled Apparatus for Gas Permeability under Low Gas Pressure

Contact Info

Product

Resources

About