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Analyzing the effect of various soil properties on the estimation of soil specific surface area by different methods

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Title Analyzing the effect of various soil properties on the estimation of soil specific surface area by different methods
 
Creator BAYAT, H
EBRAHIMI, E
ERSAHIN, S
HEPPER, EN
SINGH, DN
AMER, AMM
YUKSELEN-AKSOY, Y
 
Subject CATION-EXCHANGE CAPACITY
ARTIFICIAL NEURAL-NETWORK
WATER-RETENTION CURVES
FINE-GRAINED SOILS
PEDOTRANSFER FUNCTIONS
ORGANIC-MATTER
ATTERBERG LIMITS
LIQUID LIMIT
HYDRAULIC CONDUCTIVITY
PHYSICAL-PROPERTIES
Artificial neural network
Group method of data handling
Pedotransfer functions
Regression trees
Specific surface area
Sensitivity analysis
 
Description Depending on the method used, measuring the specific surface area (SSA) can be expensive and time consuming and limited numbers of studies have been conducted to predict SSA from soil properties. In this study, 127 soil sample data were gathered from the available literature. The data set included SSA values and some of the soil physical and chemical index properties. At the first step, linear regression, non-linear regression, regression trees, artificial neural networks, and a multi-objective group method of data handling were used to develop seven pedotransfer functions (PTFs) for the purpose of finding the best method in predicting SSA. Results showed that the artificial neural networks performed better than the other methods used in the development and validation of PTFs. At the second step, to find the best set of SSA for predicting input variables and to investigate the importance of the input parameters, the artificial neural networks were further used and 25 models were developed. The results showed that the PTF, containing the input variables of sand%, clay%, plastic limit, liquid limit, and free swelling index performed better than the other PTEs. This can be attributed to the close relation between the free swelling index and Atterberg limits with the soil clay mineralogy, which is one of the most important factors controlling SSA. The sensitivity analysis showed that the greatest sensitivity coefficients were found for the cation exchange capacity, clay content, liquid limit, and plasticity index in different models. Overall, the artificial neural networks method was proper to predict SSA from soil variables. (C) 2015 Elsevier B.V. All rights reserved.
 
Publisher ELSEVIER SCIENCE BV
 
Date 2016-01-14T13:21:37Z
2016-01-14T13:21:37Z
2015
 
Type Article
 
Identifier APPLIED CLAY SCIENCE, 116,129-140
0169-1317
1872-9053
http://dx.doi.org/10.1016/j.clay.2015.07.035
http://dspace.library.iitb.ac.in/jspui/handle/100/17602
 
Language en