Using Pedotransfer Functions to Predict Saturated Hydraulic Conductivity for Layered Soil Profiles a Case Study at the Sokoine University of Agriculture Farm, Tanzania

2003 ◽  
Vol 26 (2) ◽  
pp. 96-106
Author(s):  
M.K. Mulengera ◽  
M.V Mdemu ◽  
W.P Mtakwa
Keyword(s):  
Hydraulic Conductivity ◽  
Layered Soil ◽  
Saturated Hydraulic Conductivity ◽  
Pedotransfer Functions ◽  
Soil Profiles

scholarly journals Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 705
Author(s):  
Josué Trejo-Alonso ◽  
Carlos Fuentes ◽  
Carlos Chávez ◽  
Antonio Quevedo ◽  
Alfonso Gutierrez-Lopez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hydraulic Conductivity ◽  
Input Data ◽  
Logistic Function ◽  
Saturated Hydraulic Conductivity ◽  
Irrigation District ◽  
Pedotransfer Functions ◽  
Back Propagation Algorithm ◽  
Artificial Neural

In the present work, we construct several artificial neural networks (varying the input data) to calculate the saturated hydraulic conductivity (KS) using a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. All of them were constructed using two hidden layers, a back-propagation algorithm for the learning process, and a logistic function as a nonlinear transfer function. In order to explore different arrays for neurons into hidden layers, we performed the bootstrap technique for each neural network and selected the one with the least Root Mean Square Error (RMSE) value. We also compared these results with pedotransfer functions and another neural networks from the literature. The results show that our artificial neural networks obtained from 0.0459 to 0.0413 in the RMSE measurement, and 0.9725 to 0.9780 for R2, which are in good agreement with other works. We also found that reducing the amount of the input data offered us better results.

Evaluation of Pedotransfer Functions for Predicting Saturated Hydraulic Conductivity for U.S. Soils

2009 ◽  
Author(s):  
Ahmed M Abdelbaki ◽  
Mohamed A Youssef ◽  
Esmail M. F Naguib ◽  
Mohamed E Kiwan ◽  
Emad I El-giddawy
Keyword(s):  
Hydraulic Conductivity ◽  
Saturated Hydraulic Conductivity ◽  
Pedotransfer Functions

Global Mapping of Soil Saturated Hydraulic Conductivity Combining Legacy Data, Spatial Covariates and Machine Learning

2021 ◽  
Author(s):  
Surya Gupta ◽  
Peter Lehmann ◽  
Andreas Papritz ◽  
Tomislav Hengl ◽  
Sara Bonetti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hydraulic Conductivity ◽  
Cross Validation ◽  
Arable Land ◽  
Remotely Sensed ◽  
Saturated Hydraulic Conductivity ◽  
Pedotransfer Functions ◽  
Data Set ◽  
Global Database ◽  
Spatially Distributed

<p>Saturated soil hydraulic conductivity (Ksat) is a key parameter in many hydrological and climatic modeling applications, as it controls the partitioning between precipitation, infiltration and runoff. Values of Ksat are often deduced from Pedotransfer Functions (PTFs) using maps of soil attributes. To circumvent inherent limitations of present PTFs (heavy reliance of arable land measurements, ignoring soil structure, and geographic bias to temperate regions), we propose a new global Ksat map at 1–km resolution by harnessing technological advances in machine learning and availability of remotely sensed surrogate information (terrain, climate and vegetation). We compiled a comprehensive Ksat data set with 13,258 data geo-referenced points from literature and other sources. The data were standardized and quality-checked in order to provide a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB was then applied to develop a Covariate-based GeoTransfer Function (CoGTF) model for predicting spatially distributed Ksat values using remotely sensed information on various environmental covariates. The model accuracy assessment based on spatial cross-validation shows a concordance correlation coefficient (CCC) of 0.16 and a root meansquare error (RMSE) of 1.18 for log10 Ksat values in cm/day (CCC=0.79 and RMSE=0.72 for non spatial cross-validation). The generated maps of Ksat represent spatial patterns of soil formation processes more distinctly than previous global maps of Ksat based on soil texture information and bulk density. The validation indicates that Ksat could be modeled without bias using CoGTFs that harness spatially distributed surface and climate attributes, compared to soil information based PTFs. The relatively poor performance of all models in the validation (low CCC and high RMSE) highlights the need for the collection of additional Ksat values to train the model for regions with sparse data.</p>

MONOLITH COLUMNS FOR STUDYING ORGANIC SOIL PROFILES

1986 ◽  
Vol 66 (4) ◽  
pp. 737-742
Author(s):  
J. A. MILLETTE ◽  
R. S. BROUGHTON
Keyword(s):  
Hydraulic Conductivity ◽  
Physical Properties ◽  
Bulk Density ◽  
Organic Soil ◽  
Saturated Hydraulic Conductivity ◽  
Fiber Content ◽  
Organic Soils ◽  
Soil Profiles ◽  
Sampling Procedures ◽  
Monolith Columns

Monolith column construction and sampling procedures were described for organic soil profiles and used to measure the variation with depth of saturated hydraulic conductivity, bulk density and fiber content. The top 0.30 m of the organic soil was more permeable, had a greater bulk density and had a greater fiber content than the soil layer between 0.60 and 0.90 m from the soil surface. These columns can be used for correlations studies between physical properties and studies of the dynamic nature of the physical properties of organic soils. Key words: Saturated hydraulic conductivity, bulk density, fiber content, organic soil, monolith columns

scholarly journals Comparison of pedotransfer functions for the determination of saturated hydraulic conductivity coefficient

2017 ◽  
Vol 28 (1) ◽  
pp. 25-30 ◽  
Author(s):  
Marek Ryczek ◽  
Edyta Kruk ◽  
Magdalena Malec ◽  
Sławomir Klatka
Keyword(s):  
Hydraulic Conductivity ◽  
Direct Methods ◽  
Laboratory Method ◽  
Saturated Hydraulic Conductivity ◽  
Pedotransfer Functions ◽  
United States Bureau ◽  
Empirical Methods ◽  
Mean Values ◽  
Bureau Of Reclamation

Abstract On one hand, direct methods of measurement of saturated hydraulic conductivity coefficient are time consuming, and on the other hand, laboratory methods are cost consuming. That is why the popularity of empirical methods has increased. Their main advantages are speed of calculations and low costs. Comparison of various empirical methods (pedotransfer functions) for the determination of saturated hydraulic conductivity coefficient was the purpose of this work. The methods used were Shepard’s, Hazen’s, USBR (United States Bureau of Reclamation), Saxton et al.’s, Kozeny–Carman’s, Krüger’s, Terzaghi’s, Chapuis’s, Sheelheim’s, Chapuis’, and NAVFAC (Naval Facilities Engineering Command) methods. Calculations were carried out for the soil samples of differential texture. The obtained results shows the methods used for the determination of permeability coefficient differ considerably. Mean values obtained by analysed methods fluctuated between 0.0006 and 12.0 m·day−1. The results of calculations by the chosen methods were compared with the results of the laboratory method. The best compatibility with laboratory method was obtained by using the Terzaghi method.

Spatial variation of saturated hydraulic conductivity in a riparian zone: A case study of the Baoan Lake in Hubei Province, China

CATENA ◽  
2022 ◽  
Vol 210 ◽  
pp. 105891
Author(s):  
Zhuo Zeng ◽  
Jihong Xia ◽  
Shunan Dong ◽  
Wangwei Cai ◽  
Chuanbin Dou ◽  
...  
Keyword(s):  
Hydraulic Conductivity ◽  
Spatial Variation ◽  
Riparian Zone ◽  
Hubei Province ◽  
Saturated Hydraulic Conductivity
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