JWSS - Journal of Water and Soil Science

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Pedotransfer functions are the predictive models of a certain soil property from other easily, routinely, or cheaply measured properties. The common approach for fitting the pedotransfer functions is the use of the conventional statistical regression method. Such an approach is heavily based on the crisp obervations and also the crisp relations among variables. In the modeling natural systems, like soil, we are dealing with imprecise observations and the vague relations among the variables. Therefore, we need an appropriate algorithm for modeling such a fuzzy structures. In the present study, the fuzzy regression approach was used in order to fit some chemical and physical pedotransfer functions. The optimum regression models with the fuzzy coefficients were obtained for modeling pedotransfer functions. Sensivity analysis was carried out by using the credibility level. The results indicated that the fuzzy regression might be considered, as a suitable alternative or a complement to the statistical regression, whenever a relationship between variables is imprecise and generally when dealing with the errors due to a vaguness in regression models.

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Soil fertility measures such as cation exchange capacity (CEC) may be used in upgrading soil maps and improving their quality. Direct measurement of CEC is costly and laborious. Indirect estimation of CEC via pedotransfer functions, therefore, may be appropriate and effective. Several delineations of two consociation map units consisting of two soil families, Shahrak series and Chaharmahal series, located in Shahrekord plain were identified. Soil samples were taken from two depths of 0-20 and 30-50 cm and were analyzed for several physico-chemical properties. Clay and organic matter percentages as well as moisture content at -1500 kPa correlated best with CEC. Pedotransfer functions were successfully developed using regression and artificial neural networks. In this research, it seemed that one hidden layer with one node was sufficient for all neural networks models. The best regression model consisting of organic matter and clay variables showed R2=0.81 and RMSE=7.2 while best corresponding neural network with a learning coefficient of 0.3 and an epoch of 40 had R2=0.88 and RMSE=0.34. Data partitioning according to soil series and soil depths increased the accuracy and precision of the functions. Compared to regression, artificial neural network technique gave pedotransfer functions with greater R2 and smaller RMSE.

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Measuring the cation exchange capacity (CEC) as one of the most important chemical soil properties is very time consuming and costly. Pedotransfer functions (PTFs) provide an alternative to direct measurement by estimating CEC. The objective of this study was to develop PTFs for predicting CEC of Guilan province soils using artificial neural network (ANN) and multiple-linear regression method and also determine whether grouping based on soil textural class and organic carbon content improved estimating CEC by two methods. For this study, 1662 soil samples of Guilan province were used from soil chemistry laboratory database of Rice Research Institute. 1109 data were used for training (the development of PTFs) and 553 data for testing (the validation of PTFs) of the models. The results showed that organic carbon was the most important variable in the estimation of cation exchange capacity for total data and all classes in textural and organic C groups in both methods. ANN performed better than the regression method in predicting CEC in all data, and grouping of data only improved the prediction of PTFs in Sand and Sandy clay loam classes by ANN method.

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Soil water characteristic curve shows the relationship between soil water content and matric suction, which has an important role in water movement in the soil. The measurement of this curve is expensive and time-consuming in laboratory therefore, many methods have been proposed for its estimation including pedotransfer functions. By using the pedotransfer functions, soil water characteristic curve can be estimated based on other easily measured soil physicochemical properties. Parametric pedotransfer functions have been offered for parameters of the existing soil water characteristic curve models. In this study, 12 internal and external parametric pedotransfer functions of Brooks and Corey, Campbell and van Genuchten models were used and evaluated for 30 top soil samples in Fars province. To this end, the soil water characteristic curve and other necessary soil properties were measured, and then all soils according to the texture were divided into three groups of fine, medium and course textures. The results showed that the parametric pedotransfer functions of van Genuchten model were better than the other models, beacause of the better fit of this model to the measured data. Also, the results demonstrated that the parametric pedotransfer functions of Wosten et al. were the most appropriate method for estimating the soil water characteristic curve for the selected soils in Fars province, and that internal pedotransfer functions were not appropriate

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Field capacity and permasent wilting point are the most important parameters in designing and programming irrigation, whose measurements are troublesome and time-consuming. But these parameters could be estimated by easy data characteristics such as soil texture, organic matter and gypsum, using Pedotransfer Functions (PTFs) with high precision. In order to estimate soil moisture at FC and PWP by easy data characteristic, using neural network (ROSETA) and regression models ,20 soil samples with 6 replications were collected from around Bardsir area in Kerman province and the charactersifics including, bulk density, clay, sand, silt, FC, PWP,T.N.V and organic matter were determined for each sample. The results showed that progress in neural network from a low level up to higher level needs new inputs (charactersifics), but without any considerable increase in the precision of prediction. Also, regression analysis for estimation of linear models to predict FC and PWP showed that PWP has a significant positive correlation with clay, and FC significantly correlated with sand, silt and clay. Therefore, two prediction models were constructed for FC and PWP with (R2= 69.2) and (R2= 76.6), respectively.

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Direct measurement of soil hydraulic characteristics is costly and time-consuming. Also, the method is partly unreliable due to soil heterogeneity and laboratory errors. Instead, soil hydraulic characteristics can be predicted using readily available data such as soil texture and bulk density using pedotransfer functions (PTFs). Artificial neural networks (ANNs) and statistical regression are two methods which are used to develop PTFs. In this study, the multi-layer perceptron (MLP) neural network and backward and stepwise regression models were used to estimate saturated hydraulic conductivity using some soil characteristics including the percentage of particle size distribution, porosity, and bulk density. Data of 125 soil profiles were collected from the reports of basic soil science and land reclamation studies conducted by Khuzestan Water and Power Organization. The results showed that MLP neural network having Bayesian training algorithm with the greater coefficient of determination (R2=0.65) and the lower error (RMSE =0.04) had better performance than multiple linear regression model in predicting saturated hydraulic conductivity.

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Saturated hydraulic conductivity (Kfs) and macroscopic capillary length of soil pores are important hydraulic properties for water flow and solute transport modeling. Measuring these parameters is tedious, time consuming and expensive. One way is using indirect methods such as Pedotransfer functions (PTFs). The objective of this research was to develop some PTFs for estimating saturated hydraulic conductivity and inverse of macroscopic capillary length parameters (*). Therefore, the coefficients, Kfs and * from 60 points of Azadegan plain in Shahrekord were measured using single ring and multiple constant head method. Also, some of the readily available soil parameters from the two first pedogenic layers of the soils were obtained. Then, the desired PTFs were developed using stepwise multiple linear regression. The accuracy and reliability of the derived PTFs were evaluated using root mean square error (RMSE), mean error (ME), relative error (RE) and Pearson correlation coefficient (r). The highest correlation coefficients of 0.92 and 0.72 were found between Kfs-bulk density and *-bulk density, respectively. There was no significant correlation between soil particle size distribution and Kfs and *. This can be related to the fact that most of the soil samples were similar in texture and macro pores. The most efficient PTFs in predicting Kfs and * could explain 85 and 66 percent of the variability of these parameters, respectively. All the derived PTFs underestimated the Kfs and * parameters.

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Cation exchange capacity (CEC) is one of the important indices in soil fertility. Direct measurement of CEC is time consuming and expensive, especially in aridisols containing high amounts of carbonates and gypsum. Alternatively, CEC could be indirectly predicted through pedotransfer functions (PTF). The objective of this study was to predict CEC using class and continuous PTFs.A data set (n = 977) was classified according to the soil textural class and was used to derive the PTFs. Another independent set (n = 173) was used to test the reliability of the PTFs. The root mean of square error (RMSE), mean error (ME) and index of agreement (d) were applied to evaluate the PTFs. Within every textural class, we furthermore evaluated the relative improvement (RI) of the continuous PTFs over the corresponding class PTF. The continuous PTFs were more accurate than class PTFs for finer textural classes while the former showed higher reliability in coarser textural classes. With an increase in relative particle size, prediction bias of class PTFs decreased RMSE was 8.55 and 3.88 in clay and sandy loam textural classes, respectively. Consequently, according to the results obtained in this study, for the prediction of soil CEC, continuous PTFs are suggested to be used for silty loam and finer textural classes while for loam and coarser classes application of class PTFs is preferred.

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Treated wastewater may influence soil structure, porosity and as a consequence, soil saturated hydraulic conductivity. This study aims to assess the effect of wastewater on saturated hydraulic conductivity; and to determine the suitable soil solids fractal dimension to incorporate into the pedotransfer function by Rawls et al (1993) for estimation of saturated hydraulic conductivity (Ks). Soil saturated hydraulic conductivity was measured by disc permeameter. Soil particle fractal dimension was calculated from linearized forms of mass- time, mass- diameter and mass- diameter as modified by Kravchenko- Zhang (1998) relations. Wastewater irrigation for 13 years increased the saturated hydraulic conductivity three times, from 7 mm/hour to 21 mm/hour, but longer application of wastewater did not further increase it. Rawls et al (1993) pedotransfer produced acceptable and relatively close saturated hydraulic conductivity values to that of disc permeameter when fractal dimension obtained from the linearized forms of mass- diameter and Kravchenko- Zhang relations were used. Therefore, Rawls et al (1993) pedotransfer was capable of reflecting the effect of wastewater application on soil saturated hydraulic conductivity.