ESTIMATION OF SOIL PHYSICAL PROPERTIES USING PEDOTRANSFER FUNCTIONS IN THE BANK OF YANGTZE RIVER OF CHINA

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1 Telfth International Water Technology Conference, IWTC , Alexanria, Egypt 1577 ESTIMATION OF SOIL PHYSICAL PROPERTIES USING PEDOTRANSFER FUNCTIONS IN THE BANK OF YANGTZE RIVER OF CHINA S. H. Tabatabaei 1, GM. Xu 2 an P. Najafi 3 1 Assistant Professor, Department of Water Engineering, Faculty of Agriculture, Shahrekor University, Shahrekor, Iran stabaei@agr.sku.ac.ir, (Member of ASCE) 2 Research Assistant, Nanjing hyraulic research institute, Geotechnical Engineering Department, Nanjing, P.R. China gmxu@njhri.eu.cn 3 Assistant Professor, Department of Soil Science, Faculty of Agriculture, Khorasgan Islamic Aza University, Isfahan, Iran payam.najafi@gmail.com ABSTRACT One of the ne techniques for preication of the soil physical properties (SPP) is Peotransfer function (PTF). Generally, in this approach the SPP such as ry ensity, porosity, voi ratio, soil hyraulic conuctivity estimate by a semi-empirical equation. The problem is that the PTF estimation is more accurate for the region that the PTF as carrie out an the number of the ata hich as been use. The objective of this research as eveloping some PTF for estimation SPP in bank of the Yangtze River, in Nanjing city, Jiangsu province, China. The SPP that consiere in this research ere: et ensity ( ρ ), ry ensity ( ρ ), voi ratio (e), liqui limit (L L ) an plastic limit (L P ). All soil analysis carrie out by the soil geotechnical analysis stanar metho. There ere use 650 series of ata for calibration an more 100 series ata for verification. The result shos that most of SPP in the stuy area can be significantly estimate by et ensity ( ρ ). For instant ρ = ρ an Ll = ρ. Base on the result, a computer program evelope to estimate SPP. Keyors: Soil physical parameter, peotransfer function, Yangtze River, Nanjing. INTRODUCTION A broa array of methos currently exists to etermine soil physical properties (SPP) in the fiel or in the laboratory. While measurements permit the most exact etermination of soil physical properties, they often require a substantial investment in both time an money.

2 1578 Telfth International Water Technology Conference, IWTC , Alexanria, Egypt Moreover, many vaose zone stuies are concerne ith large areas of lan that may exhibit substantial spatial variability in the soil hyraulic properties. It is virtually impossible to perform enough measurements to be meaningful in such cases, thus inicating a nee for inexpensive an rapi ays to etermine soil hyraulic properties (Schaap et al., 2001). Many inirect methos for etermining soil physical properties have been evelope in the past. Most of these methos can be classifie as peotransfer functions (PTF) because they translate existing surrogate ata (e.g. particle-size istributions, bulk ensity an organic matter content) into soil physical ata. All PTFs have a strong egree of empiricism in that they contain moel parameters that ere calibrate on existing soil physical atabases. A PTF can be as simple as a lookup table that gives physical parameters accoring to textural class or inclue linear or nonlinear regression equations. PTFs ith a more physical founation exist, such as the poresize istribution moels by Burine (1953) an Mualem (1976), hich offer a metho to calculate unsaturate hyraulic conuctivity from ater retention ata. Moels by Haverkamp an Parlange (1986) an Arya an Paris (1981) use the shape similarity beteen the particle an pore-size istributions to estimate ater retention. Tyler an Wheatcraft (1989) combine the Arya moel ith fractals mathematics, hile Arya recently extene the similarity approach to estimate ater retention an unsaturate hyraulic conuctivity. Since PTFs are often evelope empirically, their applicability may be limite to the ata set use to efine the metho (Donatelli et al., 1996 an Wosten et al., 1999). Neural netork analysis has also been use to establish empirical PTFs (Pachepsky et al., 1996; Schaap an Leij, 1998; Schaap et al., 1998; Minasny an McBratney, 2002). An avantage of neural netorks over traitional PTFs is that they o not require a priori moel concept. The optimal an possibly nonlinear relations that link input ata (particle size ata an bulk ensity, etc.) to output ata (Liqui limit, hyraulic parameters, etc) are obtaine an implemente in an iterative calibration proceure. As a result, neural netork moels typically extract the maximum amount of information from the ata (Schaap et al., 2001). Rosetta uses a neural netork for preiction an the bootstrap approach to perform uncertainty analysis SOILPAR2 can compute estimates of soil hyrological parameters by several proceures, an compares the estimates ith measure ata using statistical inices an graphs (Givi et al. 2004). The objective of this paper is to evelop several peotransfer functions in estimating some soil physical properties in the natural soils in the bank of Yangtze River in Nanjing City, Jiangsu provience, China.

3 Telfth International Water Technology Conference, IWTC , Alexanria, Egypt 1579 MATERIALS AND METHODS Materials The base material of this research is soil geotechnical analysis that collecte from the library of Nanjing Hyraulic Research Institute (NHRI) an epartment of structure an ater resource of Hohahi University, Nanjing, P. R. China. The soil samples ere selecte from ifferent epth of the natural soil in bank of Yangtze River at Jiangsu province, Nanjing City, China at June Figure 1 shos the location of soil sample. Sampling location Figure 1- Soil sampling location in the eastern part of China The total number of ata as 750 series. Another material of this research as computer softare such as spreasheet (Excel) an statistical softare (Curve expert, SPSS). Methos The soil physical properties ere: et ensity ( ), ry ensity ( ), voi ratio (e), porosity (n), elastic limit (El), plastic limit (PL) an plasticity inex (PI). All soil analysis carrie out by the soil geotechnical stanar metho. There is use 650 series of ata for calibration an the other ata (100) as use for verification. The process hich use for ata analysis an riving PTF as as follo: 1- Selecting to parameters (for example an ), 2- Classification of the ata, 3- Driving PTF by curve expert from the 650 series, 4- Analysis of variance (ANOVA) for PTF by SPSS, 5- Preication of target parameter by the 100 remaining ata points an PTF, 6- Paire ifference sample analysis of observe an preicte ata

4 1580 Telfth International Water Technology Conference, IWTC , Alexanria, Egypt It use to statistical analyses for this ata. The first as analysis of variance for estimation of a liner or nonlinear equation. The secon one as analysis of variance for efinition of ifference beteen the preicte parameter through the equation an observe ata. RESULTS AND DISCUSSION As the et ensity is the first parameter an more popular than the other parameters that is measure in soil geotechnical analysis, so it as trie to estimate another parameter base on et ensity. Dry Density The analysis as one on ry ensity. It use 650 series ata for calibration. Figure 2 shos the ry ensity versus et ensity for the calibration ata as ell as the resiual for the ata. Dry ensity (gr/cm 3 ) ry ensity Linear (ry ensity) R 2 = Wet ensity (gr/cm 3 ) Resiuals Wet ensity (gr/cm3) Figure 2- The relationship of et vs. ry ensity (left) an resiual of PTF (right) The result shos a PTF for the ry ensity, as Eq. (1). It shos that the correlation coefficient beteen ρ an ρ is more than 0.98: ρ ρ = (1) Analysis of variance (ANOVA) is one for the ry ensity ata. It is shon in Table 1.

5 Telfth International Water Technology Conference, IWTC , Alexanria, Egypt 1581 Table 1- Analysis of variance for ry ensity estimation Sum of Squares f Mean Square F Sig. Regression Resiual Total It use 100 series ata for the verification. For testing the ability of the ata for preication of ρ, the verification technique as use. The result of the verification is presente in Fig. 3. Dry ensity (gr/cm 3 ) Wet ensity (gr/cm 3 ) Figure 3- Comparison of the observe an estimate ry ensity for the verification ata The paire sample ifference analysis (PSDA) test one by the SPSS softare that the result shon in Table 1. The average square error (ASE) of the estimate is It means that the PTF coul estimate the ry ensity ith a high accuracy. The result shos that the stanar eviation beteen the observe an preicate ata is less than It confirms the last result too.

6 1582 Telfth International Water Technology Conference, IWTC , Alexanria, Egypt Mean St. Deviation Table 2- Paire samples ifferences test (PSDT) Paire Differences T Df Sig. (2-taile) St. Error Mean 95% Confience Interval of the Difference Loer Upper Voi Ratio The result shos the folloing equation for estimation of voi ratio base on et ensity: 1 e = ρ ρ (2) 2 Figure 4 shos the voi ratio versus et ensity for the calibration ata as ell as resiual for the ata Resiuals Voi Ratio (% ) Voi ratio (%) Wet ensity (gr/cm 3 ) Wet ensity (gr/cm3) Figure 4-The relationship of et ensity vs. voi ratio (left) an resiual of PTF (right) The average square error (ASE) of the estimate is It means that the PTF coul estimate the voi ratio ith a high accuracy. The result of the verification is presente in Fig. 5.

7 Telfth International Water Technology Conference, IWTC , Alexanria, Egypt 1583 Observe Estimate 2.4 Voi ratio(%) Wet ensity (gr/cm3) Figure 5- Comparison of the observe an estimate ata for the voi ratio Liqui Limit The similar proceure as carrie out for liqui limit (L L ). Figure 6 shos the liqui limit vs. et ensity. The PTF, Eq. (3) is offere for the estimation of L L : Ll = ρ (3) It shos that R square is 0.77 an stanar error is 5.33 for the calibration ata. It can conclue that best PTF has lo accuracy. So it as expectable that the ASE for estimation process as relatively high (22.63) for the verification ata. The result shos that the PTF has lo but acceptable accuracy for estimation of L L. It as trie to fin a more accurate PTF ith other parameters (e.g. Dry ensity, voi ratio) but it not foune. liqui limit (%) Wet ensity (gr/cm 3 ) Liqui limit (%) Resiuals Wet ensity (gr/cm3) Figure 6- The relationship of et ensity vs. liqui limit (left) an resiual of PTF (right)

8 1584 Telfth International Water Technology Conference, IWTC , Alexanria, Egypt Observe Estimate Liqui limit (%) Wetensity (gr/cm 3 ) Figure 7- Comparison of the observe an estimate ata for the liqui limit Plastic Limit The same proceure i for Plastic limit (P L ). The result shos that no acceptable PTF can be foun for P L Vs et ensity. So it as trie to fin a PTF ith other parameter. The result shos that it can fin a meaningful PTF on P L an L L. Figure 8 shos the P L vs. L L. It shos that R square is 0.90 an stanar error is Plastic limit (%) plastic limit Linear (plastic limit) Liqui limit (%) plastic limit (%) Resiuals liqui limit (%) Figure 8- The relationship of liqui limit vs. plastic limit (left) an resiual of PTF (right) Eq. (4) shos the PTF for estimation of P L base on L L. P = (4) l L l The average square error (ASE) of the estimation of P L is It can be seen in Fig. 9 the result of verification process.

9 Telfth International Water Technology Conference, IWTC , Alexanria, Egypt 1585 Observe Estimate Plastic limit (%) Liqui Limit (%) Figure 9- Comparison of the observe an estimate ata for the Plastic limit Base on the result of this research, a computer program (SPPEN) as evelope on Visual Basic 6.0 for estimation of the soil physical properties. ACKNOWLEDGMENT The Authors thank to Black Polan, University of Marylan for his help on this paper. REFERENCES 1. Arya, L.M. an Paris, J.F A physicoempirical moel to preict the soil moisture characteristic from particle-size istribution an bulk ensity ata. SSSAJ 45: Burine, N.T Relative permeability calculation from size istribution ata. Trans. AIME 198: Curve expert, 2007, a comprehensive curve fitting system for inos, 4. Donatelli, M., Acutis, M. an Laruccia, N., Peotransfer functions: evaluation of methos to estimate soil ater content at fiel capacity an ilting point..isci.it/mon/research/bottom_moeling_cs.htm pp Givi, J., Prasher, S.O. an Patel, R.M. 2004, Evaluation of peotransfer functions in preicting the soil ater contents at fiel capacity an ilting point, Agricultural Water Management 70 (2): Haverkamp, R. an Parlange, J.Y., Preicting the ater-retention curve from a particle-size istribution: 1. Sany soils ithout organic matter. Soil. Sci. 142: Minasny, B. an McBratney, A.B The Neuro-m metho for fitting neural netork parametric peotransfer functions, SSSAJ 66: Mualem, Y A ne moel preicting the hyraulic conuctivity of unsaturate porous meia, Water Resour. Res. 12: Pachepsky, Y.A., Timlin, D. an Varallyay, G Artificial neural netorks to estimate soil ater retention from easily measurable ata, SSSAJ 60: Schaap, M.G. an Leij, F.J Database relate accuracy an uncertainty of peotransfer functions, Soil Sci. 163: Schaap, M.G., Leij, F.J. an VanGenuchten, M.T Neural netork analysis for hierarchical preiction of soil ater retention an saturate hyraulic conuctivity, SSSAJ 62: Schaap, M.G., Leij, F.J., an VanGenuchten, M.T Rosetta: a computer program for estimating soil hyraulic parameters ith hierarchical peotransfer functions. J. Hyrol. 251: SPSS, 2006, Statistical softare, SPSS Ver (.spss.com). 14. Tyler, W.S. an Wheatcraft S.W Application of fractal mathematics to soil ater retention estimation. SSSAJ 3: Wosten, H.M., Lilly, A. Nemes A. an Bas, C.L Development an use of a atabase of hyraulic properties of European soils, Geoerma 90: