scholarly journals Artificial Neural Networks for Predicting the Water Retention Curve of Sicilian Agricultural Soils

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1431 ◽  
Author(s):  
Alessandro D’Emilio ◽  
Rosa Aiello ◽  
Simona Consoli ◽  
Daniela Vanella ◽  
Massimo Iovino
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soil Water ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Water Retention Curve ◽  
Retention Data ◽  
Soil Hydraulic Properties ◽  
Retention Curve ◽  
Artificial Neural

Modeling soil-water regime and solute transport in the vadose zone is strategic for estimating agricultural productivity and optimizing irrigation water management. Direct measurements of soil hydraulic properties, i.e., the water retention curve and the hydraulic conductivity function, are often expensive and time-consuming, and represent a major obstacle to the application of simulation models. As a result, there is a great interest in developing pedotransfer functions (PTFs) that predict the soil hydraulic properties from more easily measured and/or routinely surveyed soil data, such as particle size distribution, bulk density (ρb), and soil organic carbon content (OC). In this study, application of PTFs was carried out for 359 Sicilian soils by implementing five different artificial neural networks (ANNs) to estimate the parameter of the van Genuchten (vG) model for water retention curves. The raw data used to train the ANNs were soil texture, ρb, OC, and porosity. The ANNs were evaluated in their ability to predict both the vG parameters, on the basis of the normalized root-mean-square errors (NRMSE) and normalized mean absolute errors (NMAE), and the water retention data. The Akaike’s information criterion (AIC) test was also used to assess the most efficient network. Results confirmed the high predictive performance of ANNs with four input parameters (clay, sand, and silt fractions, and OC) in simulating soil water retention data, with a prediction accuracy characterized by MAE = 0.026 and RMSE = 0.069. The AIC efficiency criterion indicated that the most efficient ANN model was trained with a relatively low number of input nodes.

Combination of artificial neural networks and fractal theory to predict soil water retention curve

2013 ◽  
Vol 92 ◽  
pp. 92-103 ◽  
Author(s):  
Hossein Bayat ◽  
Mohammad Reza Neyshaburi ◽  
Kourosh Mohammadi ◽  
Nader Nariman-Zadeh ◽  
Mahdi Irannejad ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soil Water ◽  
Water Retention ◽  
Fractal Theory ◽  
Water Retention Curve ◽  
Soil Water Retention Curve ◽  
Soil Water Retention ◽  
Retention Curve ◽  
Artificial Neural

Predicting water retention curves of fine texture soils from particle size distribution

2020 ◽  
Author(s):  
Joseph Pollacco ◽  
Jesús Fernández-Gálvez ◽  
Sam Carrick
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Pore Space ◽  
Water Retention Curve ◽  
Soil Hydraulic Properties ◽  
Retention Curve ◽  
Soil Particles

<p>Indirect methods for estimating soil hydraulic properties from particle size distribution have been developed due to the difficulty in accurately determining soil hydraulic properties, and the fact that particle size distribution is one piece of basic soil physical information normally available. The similarity of the functions describing the cumulative distribution of particle size and pore size in the soil has been the basis for relating particle size distribution and the water retention function in the soil. Empirical and semi-physical models have been proposed, but these are based on strong assumptions that are not always valid. For example, soil particles are normally assumed to be spherical, with constant density regardless of their size; and the soil pore space has been described by an assembly of capillary tubes, or the pore space in the soil matrix is assumed to be arranged in a similar way regardless of particle size. However, in a natural soil the geometry of the pores may vary with the size of the particles, leading to a variable relation between particle radius and pore radius.</p><p> </p><p>The current work is based on the hypothesis that the geometry of the pore size and the void ratio depends on the size of the soil particles, and that a physically based model can be generalised to predict the water retention curve from particle size distribution. The rearrangement of the soil particles is considered by introducing a mixing function that modulates the cumulative particle size distribution, while the total porosity is constrained by the saturated water content.</p><p> </p><p>The model performance is evaluated by comparing the soil water retention curve derived from laboratory measurements with a mean Nash–Sutcliffe model efficiency a value of 0.92 and a standard deviation of 0.08. The model is valid for all soil types, not just those with a marginal clay fraction.</p>

Impact of plant roots and soil organisms on soil micromorphology and hydraulic properties

Biologia ◽  
2006 ◽  
Vol 61 (19) ◽  
Author(s):  
Radka Kodešová ◽  
Vít Kodeš ◽  
Anna Žigová ◽  
Jiří Šimůnek
Keyword(s):  
Pore Structure ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Water Retention Curve ◽  
Soil Organisms ◽  
Soil Hydraulic Properties ◽  
Soil Water Retention Curve ◽  
Soil Water Retention ◽  
Retention Curve ◽  
The Impact

AbstractA soil micromorphological study was performed to demonstrate the impact of soil organisms on soil pore structure. Two examples are shown here. First, the influence of earthworms, enchytraeids and moles on the pore structure of a Greyic Phaeozem is demonstrated by comparing two soil samples taken from the same depth of the soil profile that either were affected or not affected by these organisms. The detected image porosity of the organism-affected soil sample was 5 times larger then the porosity of the not-affected sample. The second example shows macropores created by roots and soil microorganisms in a Haplic Luvisol and subsequently affected by clay coatings. Their presence was reflected in the soil water retention curve, which displayed multiple S-shaped features as obtained from the water balance carried out for the multi-step outflow experiment. The dual permeability models implemented in HYDRUS-1D was applied to obtain parameters characterizing multimodal soil hydraulic properties using the numerical inversion of the multi-step outflow experiment.

scholarly journals Modelling of soil-water retention curve considering the effects of existing salt solution in the pore fluid

2021 ◽  
Vol 337 ◽  
pp. 02001
Author(s):  
Hamed Sadeghi ◽  
Ali Golaghaei Darzi
Keyword(s):  
Soil Water ◽  
Osmotic Potential ◽  
Water Retention ◽  
Pore Fluid ◽  
Water Retention Curve ◽  
Model Parameters ◽  
Retention Data ◽  
Soil Water Retention Curve ◽  
Soil Water Retention ◽  
Retention Curve

Soil-water retention curve (SWRC) has a wide application in geoenvironmental engineering from the predication of unsaturated shear strength to transient two-phase flow and stability analyses. Although various SWRC models have been proposed to take into account some influencing factors, less attention has been given to consider the effects of pore fluid osmotic potential. Therefore, the key objective of this study is to extend van Genchten’s model so that osmotic potential is considered as an independent factor governing the SWRC behavior. The new model comprises only six variables, which can be calibrated through minimal experimental measurements. More importantly, most of the model parameters have physical meaning by correlating macroscopic volumetric behavior and general trends of SWRC to osmotic potential. The results of validation tests revealed that the new osmotic-dependent SWRC model can predict the retention data in terms of both total and matric suction for two different soils and various molar concentrations very good. The proposed modeling approach does not require any advanced mercury intrusion porosimetry (MIP) tests, yet it can deliver excellent predictions by calibrating only six parameters which are far less than those incorporated into similar models for saline water permeating through the pore structure.

Testing an infiltrometer methodology to investigate water impact effects on both soil sealing and hydraulic properties of a loam soil under conventional tillage and no-tillage

2020 ◽  
Author(s):  
Mirko Castellini ◽  
Simone Di Prima ◽  
Anna Maria Stellacci ◽  
Massimo Iovino ◽  
Vincenzo Bagarello
Keyword(s):  
Hydraulic Conductivity ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Soil Management ◽  
Water Retention Curve ◽  
Soil Hydraulic Properties ◽  
Retention Curve ◽  
Soil Sealing ◽  
Loam Soil ◽  
Soil Hydraulic

<p>Testing new experimental procedures to assess the effects of the drops impact on the soil sealing formation is a main topic in soil hydrology.</p><p>In this field investigation, the methodological approach proposed first by Bagarello et al. (2014) was extended to account for a greater soil infiltration surface (i.e., about 3.5 times higher), a higher range and number of heights of water pouring and to evaluate the different impact on soil management. For this purpose, the effects of three water pouring heights (low, L=3 cm; medium, M=100 cm; high, H=200 cm) on both no-tilled (NT) and conventionally tilled (CT) loam soil were investigated by Beerkan infiltration runs and using the BEST-procedure of data analysis to estimate the soil hydraulic properties.</p><p>Final infiltration rate decreased when perturbing runs (i.e., M and H) were carried out as compared with the non-perturbing (L) ones (by a factor of 1.5-3.1 under NT and 3.4-4.4 under CT). Similarly, the water retention scale parameter, h<sub>g</sub>, increased (i.e., higher in absolute terms) by a factor 1.6-1.8 under NT and by a factor 1.7 under CT. Saturated hydraulic conductivity, K<sub>s</sub>, changed significantly as a function of the increase of water pouring height; regardless of the soil management, perturbing runs caused a reduction in soil permeability by a factor 5 or 6. Effects on hydraulic functions (i.e., soil water retention curve and hydraulic conductivity function), obtained with the BEST-Steady algorithm, were also highlighted. For instance, differences in water retention curve at fixed soil pressure head values (i.e., field capacity, FC, and permanent wilting point, PWP) due to perturbing and non-perturbing runs, were estimated as higher under NT (3.8%) than CT (3.4%) for FC, and equal to 2.1% or 1.6% for PWP.</p><p>Main results of this investigation confirm that a recently tilled loamy soil, without vegetation cover, can be less resilient as compared to a no-tilled one, and that tested water pouring heights methodology looks promising to mimic effects of high energy rainfall events and to quantify the soil sealing effects under alternative management of the soil.</p><p><strong>Acknowledgments</strong></p><p>The work was supported by the project “STRATEGA, Sperimentazione e TRAsferimento di TEcniche innovative di aGricoltura conservativA”, funded by Regione Puglia–Dipartimento Agricoltura, Sviluppo Rurale ed Ambientale, CUP: B36J14001230007.</p><p><strong> </strong><strong>References</strong></p><p>Bagarello, V., Castellini, M., Di Prima, S., Iovino, M. 2014. Soil hydraulic properties determined by infiltration experiments and different heights of water pouring. Geoderma, 213, 492–501. https://doi.org/10.1016/j.geoderma.2013.08.032</p>

scholarly journals Soil physical and hydraulic properties in the Donato stream basin, RS, Brazil. Part 2: Geostatistical simulation

2019 ◽  
Vol 23 (9) ◽  
pp. 675-680
Author(s):  
Tirzah M. Siqueira ◽  
José A. S. Louzada ◽  
Olavo C. Pedrollo ◽  
Nilza M. dos R. Castro ◽  
Marquis H. C. de Oliveira
Keyword(s):  
Soil Water ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Geostatistical Simulation ◽  
Sequential Gaussian Simulation ◽  
Water Retention Curve ◽  
Soil Water Retention Curve ◽  
Soil Water Retention ◽  
Retention Curve ◽  
Gaussian Simulation

ABSTRACT Geostatistical simulation has been the most promising and used technique for the analysis of uncertainties of soil physical and hydraulic properties, with high spatial heterogeneity. This study carried out a stochastic analysis of saturated hydraulic conductivity (Ksat) and soil water retention curve parameters in the Donato stream basin, located in the municipality of Pejuçara, in the state of Rio Grande do Sul, Brazil, with geographic coordinates between 28º 25’ 34” S and 53º 40’ 30” W, and 28º 24’ 50” S and 53º 41’ 30” W, 590 m of altitude. Soil samples were collected during the period from August to November of 2012. Sequential Gaussian simulation technique was used to generate 100 random fields of each variable. The results showed great uncertainties for Ksat and the parameter α of the soil water retention curve. The uncertainties between the percentiles 5 and 95% for Ksat indicated values from 24 to 44 cm d-1, and for the parameter α, the uncertainties could be estimated from 0.622 to 1.122 cm-1.

Sign in / Sign up

Export Citation Format

Share Document