Application of neural network pedotransfer functions to calculate soil water retention curve

Belik, A. A.; Bolotov, A. G.; Шеин, Е. В.; Кокорева, А. А.; Levin, A. A.; Patrushev, V.Yu.

doi:10.1088/1755-1315/368/1/012008

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…В большинстве одномерных моделей на основе дифференциальных уравнений переноса тепла и влаги в почве используются педотрансферные функции (ПТФ) -регрессионные уравнения, описывающие связь теплопроводности с традиционными химико-физическими свойствами почв: гранулометрическим составом, содержанием органического вещества, плотностью почвы и твердой фазы и др. (Belik et al, 2019). Такой подход реализован в моделях SWAP и HYDRUS-1D.…”

Section: Introductionunclassified

Thermal conductivity of urban and artificial soils: methodological aspects and mathematical modeling

Kokoreva,

Kozhunov,

Butylkina

et al. 2024

DSB

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There are various methods for experimental determination of the thermal conductivity dependence on soil moisture and substrates. The influence of the sample structure (monolith, bulk sample), sample temperature, the method of installing the probe into the sample on the obtained readings of the TEMPOS device was studied and methodological recommendations were proposed. The dependence of thermal conductivity of soils bulk samples and substrates on moisture is shown. The spread of thermal conductivity values in the moisture range from hygroscopic to full moisture capacity for soddy-podzolic soil is 0.229–1.430 W/(m*K), for peat – 0.250–0.521 W/(m*K), for sand – 0.280–2.605 W/(m*K), for a mixture – 0.234–1.568 W/(m*K). ). The influence of properties such as density, particle size distribution, specific surface area, organic matter content, salinity affected thermal properties to a lesser extent. The established patterns can be used to calculate the temperature regime of soils in solving a number of applied problems related to the construction of special soil objects, for example, when creating urban soil structures. For this, it is necessary either to determine the thermal conductivity experimentally, or to calculate it, using the physical parameters of soils and substrates. The first method is labor-consuming, the second is less accurate. As an example, the equations available for work in the HYDRUS-1D (Chang–Horton and Campbell) model are used. These equations either overestimate the thermal conductivity in the area of high substrate humidity, or underestimate the thermal conductivity in the area of low substrate humidity (sand, loam, peat and a mixture based on them).

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Section: Introductionunclassified

Thermal conductivity of urban and artificial soils: methodological aspects and mathematical modeling

Kokoreva,

Kozhunov,

Butylkina

et al. 2024

DSB

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“…As a consequence, in structured soils, sorption and degradation processes are much less important for the migration of chemicals. A significant contribution to the movement of moisture is made by soil compaction and its hydrophysical properties, which have significant spatial variability (Simsek et al 2019;Bolotov et al 2019;Belik et al 2019). These conditions are behind the nonequilibrium character of the preferential migration of substances under unsaturated conditions: due to high migration rates, the solution cannot be balanced between a circulation zone and a stagnant zone (Simunek et al 2003;Jarvis 2007;Jarvis et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Characterizing macropore structure of agrosoddy-podzolic soil using computed tomography

et al. 2020

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The agrosoddy-podzolic soil (Eutric Albic Glossic Retisol (Abruptic, Loamic, Aric, Cutanic)) is typical for Moscow Oblast and is used for agricultural purposes, resulting in use of various agrochemicals and pesticides. The presence of macropores and cracks in such soils leads to preferential water and substance transfer and nonequilibrium conditions. Therefore, it is important to study the numerical characteristics of the pore space of soils to adjust mathematical models of substance transfer. Undisturbed soil monoliths 10 cm in diameter taken from Ap (from 0 to 30 cm) and E, BE horizons (from 30 to 50 cm) were investigated under the field moisture conditions and after saturation using the tomographic core analyzer RKT-180 with the resolution of 200 μm/pixel. Using the X-ray computer tomography, it has been established that the plough layer of the agrosoddy-podzolic soil contains over 7% of macropores larger than 1 mm, while the subsurface layer has a porosity of about 3%. After saturation, some of the inter-aggregate pores overlap, which leads to a decrease in the total porosity to 4% in the upper and 2% in lower horizons, as well as increase in the average pore diameter. The number of macropores determined by tomographic analysis is one third higher than the values calculated using pedotransfer functions for this soil. The data obtained in this paper are recommended for use in national scenarios of migration of substances (pesticides, agrochemicals, salts) in soils.

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Prediction of Soil–Water Characteristic Curves in Bimodal Tropical Soils Using Artificial Neural Networks

dos Santos Pereira,

Silva Junior,

Mendes

et al. 2023

Geotech Geol Eng

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Application of neural network pedotransfer functions to calculate soil water retention curve

Cited by 4 publications

References 7 publications

Thermal conductivity of urban and artificial soils: methodological aspects and mathematical modeling

Thermal conductivity of urban and artificial soils: methodological aspects and mathematical modeling

Characterizing macropore structure of agrosoddy-podzolic soil using computed tomography

Prediction of Soil–Water Characteristic Curves in Bimodal Tropical Soils Using Artificial Neural Networks

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