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Volumn 62, Issue 1, 2016, Pages 127-138

Predicting ESP and SAR by artificial neural network and regression models using soil pH and EC data (Miankangi Region, Sistan and Baluchestan Province, Iran)

Author keywords

Artificial neural networks; EC; Linear regression; SAR

Indexed keywords


EID: 84947036007     PISSN: 03650340     EISSN: 14763567     Source Type: Journal    
DOI: 10.1080/03650340.2015.1040398     Document Type: Article
Times cited : (18)

References (41)
  • 1
    • 84863745431 scopus 로고    scopus 로고
    • Spatial variability of electrical conductivity of desert soil irrigated with treated wastewater: implications for irrigation management
    • P.Adhikari, M.K.Shukla, J.G.Mexal 2011. Spatial variability of electrical conductivity of desert soil irrigated with treated wastewater: implications for irrigation management. Appl Environ Soil. 2011:1–11.
    • (2011) Appl Environ Soil , vol.2011 , pp. 1-11
    • Adhikari, P.1    Shukla, M.K.2    Mexal, J.G.3
  • 2
    • 15744383143 scopus 로고    scopus 로고
    • Salinity-pH relationships in calcareous soils
    • A.S.Al-Busaidi, P.Cookson 2003. Salinity-pH relationships in calcareous soils. Agric Mar Sci. 8:41–46.
    • (2003) Agric Mar Sci , vol.8 , pp. 41-46
    • Al-Busaidi, A.S.1    Cookson, P.2
  • 3
    • 28244463470 scopus 로고    scopus 로고
    • Neural network models to predict cation exchange capacity in arid regions of Iran
    • M.Amini, K.C.Abbaspour, H.Khademi, N.Fathianpour, M.Afyuni, R.Schulin. 2005. Neural network models to predict cation exchange capacity in arid regions of Iran. Eur J Soil Sci. 56:551–559. doi:10.1111/j.1365-2389.2005.0698.x
    • (2005) Eur J Soil Sci , vol.56 , pp. 551-559
    • Amini, M.1    Abbaspour, K.C.2    Khademi, H.3    Fathianpour, N.4    Afyuni, M.5    Schulin, R.6
  • 5
    • 0034958009 scopus 로고    scopus 로고
    • Influence of soil properties on electrical conductivity under humid water regimes
    • K.Auerswald, S.Simon, H.Stanjek. 2001. Influence of soil properties on electrical conductivity under humid water regimes. Soil Sci. 166:382–390.
    • (2001) Soil Sci , vol.166 , pp. 382-390
    • Auerswald, K.1    Simon, S.2    Stanjek, H.3
  • 7
    • 2042483847 scopus 로고    scopus 로고
    • Pavement roughness modeling using back-propagation neural networks
    • J.-H.Choi, T.M.Adams, H.U.Bahia. 2004. Pavement roughness modeling using back-propagation neural networks. Comput Aided Civil Infrast Eng. 19:295–303.
    • (2004) Comput Aided Civil Infrast Eng , vol.19 , pp. 295-303
    • Choi, J.-H.1    Adams, T.M.2    Bahia, H.U.3
  • 9
    • 30344451377 scopus 로고    scopus 로고
    • Assessing salt-affected soils using remote sensing, solute modelling, and geophysics
    • J.Farifteh, A.Farshad, R.J.George. 2006. Assessing salt-affected soils using remote sensing, solute modelling, and geophysics. Geoderma. 130:191–206.
    • (2006) Geoderma , vol.130 , pp. 191-206
    • Farifteh, J.1    Farshad, A.2    George, R.J.3
  • 10
    • 0037127460 scopus 로고    scopus 로고
    • Determination of organic matter in soils using radial basis function networks and near infrared spectroscopy
    • P.H.Fidêncio, R.J.Poppi, J.C.De Andrade. 2002. Determination of organic matter in soils using radial basis function networks and near infrared spectroscopy. Anal Chim Acta. 453:125–134.
    • (2002) Anal Chim Acta , vol.453 , pp. 125-134
    • Fidêncio, P.H.1    Poppi, R.J.2    De Andrade, J.C.3
  • 11
    • 85075752961 scopus 로고
    • Particle-size analysis
    • Klute A., (ed), Madison (WI): American Society of Agronomy, Soil Science Society of America
    • G.W.Gee, J.W.Bauder. 1986. Particle-size analysis. In: A.Klute, editor. Methods of soil analysis. Part I. Agron. Monogr. 9. Madison (WI): American Society of Agronomy, Soil Science Society of America; p. 383–411.
    • (1986) Methods of soil analysis. Part I. Agron. Monogr. 9 , pp. 383-411
    • Gee, G.W.1    Bauder, J.W.2
  • 12
    • 84990213590 scopus 로고    scopus 로고
    • Estimation of soil salinity profile in Tabriz irrigation and drainage network using SaltMod and ANN models
    • A.Haghverdi, K.Mohammadi, S.A.Mohseni Movahed, B.Ghahreman, M.Afshar. 2011. Estimation of soil salinity profile in Tabriz irrigation and drainage network using SaltMod and ANN models. J Water Soil. 25:174–186. Persian.
    • (2011) J Water Soil , vol.25 , pp. 174-186
    • Haghverdi, A.1    Mohammadi, K.2    Mohseni Movahed, S.A.3    Ghahreman, B.4    Afshar, M.5
  • 14
    • 19844362533 scopus 로고    scopus 로고
    • Boston (MA): GNU Public License
    • R.M.Heristev. 1998. The ANN book. Boston (MA): GNU Public License.
    • (1998) The ANN book
    • Heristev, R.M.1
  • 17
    • 84947038870 scopus 로고    scopus 로고
    • The estimation of soil cation exchange capacity in disturbed and undisturbed soils using artificial neural networks and multiple regressions
    • H.Kashi, H.Ghorbani, S.Emamgholizadeh, S.A.A.Hashemi. 2013. The estimation of soil cation exchange capacity in disturbed and undisturbed soils using artificial neural networks and multiple regressions. J Water Soil. 27:472–484. Persian.
    • (2013) J Water Soil , vol.27 , pp. 472-484
    • Kashi, H.1    Ghorbani, H.2    Emamgholizadeh, S.3    Hashemi, S.A.A.4
  • 19
    • 84947028097 scopus 로고    scopus 로고
    • Prediction of amount of water at field capacity and permanent wilting point using artificial neural network and multiple regression
    • E.Mehrabi Gohari, F.Sarmadian, R.Taghizadeh Mehrjerdi. 2013. Prediction of amount of water at field capacity and permanent wilting point using artificial neural network and multiple regression. Iran J Irr Water Eng. 3:42–52.
    • (2013) Iran J Irr Water Eng , vol.3 , pp. 42-52
    • Mehrabi Gohari, E.1    Sarmadian, F.2    Taghizadeh Mehrjerdi, R.3
  • 20
    • 84947040932 scopus 로고    scopus 로고
    • SAR qualities parameter persistence by a compound method of geostatic and artificial neural network (case study of Jiroft plain)
    • S.A.Moasheri, A.Shams Goshki, A.Parsaie. 2013. SAR qualities parameter persistence by a compound method of geostatic and artificial neural network (case study of Jiroft plain). Int J Agric Crop Sci. 6:157–166.
    • (2013) Int J Agric Crop Sci , vol.6 , pp. 157-166
    • Moasheri, S.A.1    Shams Goshki, A.2    Parsaie, A.3
  • 22
    • 0030473352 scopus 로고    scopus 로고
    • Artificial neural networks to estimate soil water retention from easily measurable data
    • Y.A.Pachepsky, D.Timlin, G.Varallyay. 1996. Artificial neural networks to estimate soil water retention from easily measurable data. Soil Sci Soc Am J. 60:727–733.
    • (1996) Soil Sci Soc Am J , vol.60 , pp. 727-733
    • Pachepsky, Y.A.1    Timlin, D.2    Varallyay, G.3
  • 23
    • 0023526257 scopus 로고
    • Kinetics and mechanisms of potassium release from sandy middle Atlantic Coastal plain soils
    • M.C.Page, D.L.Sparks, M.Noll, G.J.Hendricks. 1987. Kinetics and mechanisms of potassium release from sandy middle Atlantic Coastal plain soils. Soil Sci Soc Am J. 51:1460–1465.
    • (1987) Soil Sci Soc Am J , vol.51 , pp. 1460-1465
    • Page, M.C.1    Sparks, D.L.2    Noll, M.3    Hendricks, G.J.4
  • 24
    • 0000945550 scopus 로고
    • Dry matter yield, nitrogen-15 absorption, and water uptake by green bean under sodium chloride stress
    • M.Pessarakli. 1991. Dry matter yield, nitrogen-15 absorption, and water uptake by green bean under sodium chloride stress. J Crop Sci. 31:1633–1640.
    • (1991) J Crop Sci , vol.31 , pp. 1633-1640
    • Pessarakli, M.1
  • 25
    • 84947041068 scopus 로고    scopus 로고
    • Estimation of sodium adsorption ratio (SAR) in groundwater using multivariate linear regression artificial neural networks (case study Bajestan plains)
    • H.Piri, A.Bameri. 2014. Estimation of sodium adsorption ratio (SAR) in groundwater using multivariate linear regression artificial neural networks (case study Bajestan plains). J Water Resour Eng. 21:67–80. Persian.
    • (2014) J Water Resour Eng , vol.21 , pp. 67-80
    • Piri, H.1    Bameri, A.2
  • 26
    • 58849165115 scopus 로고    scopus 로고
    • Modelling of soil cation exchange capacity based on some soil physical and chemical properties
    • M.Rashidi, M.Seilsepour. 2008. Modelling of soil cation exchange capacity based on some soil physical and chemical properties. ARPN J Agric Biol Sci. 3:6–13.
    • (2008) ARPN J Agric Biol Sci , vol.3 , pp. 6-13
    • Rashidi, M.1    Seilsepour, M.2
  • 27
    • 84947019189 scopus 로고    scopus 로고
    • Effect of different application rates of Boron on yield
    • M.Rashidi, M.Seilsepour. 2011. Effect of different application rates of Boron on yield. Middle-East J Sci Res. 7:758–762.
    • (2011) Middle-East J Sci Res , vol.7 , pp. 758-762
    • Rashidi, M.1    Seilsepour, M.2
  • 29
    • 0003288498 scopus 로고
    • The use of saline waters for crop production
    • Food and Agriculture Organisation of the United Nations, Rome, Italy:
    • J.D.Rhoades, A.Kandiah, A.M.Mashali 1992. The use of saline waters for crop production. FAO Irrigation and Drainage Paper No 48. Food and Agriculture Organisation of the United Nations; Rome, Italy.
    • (1992) FAO Irrigation and Drainage Paper No 48
    • Rhoades, J.D.1    Kandiah, A.2    Mashali, A.M.3
  • 30
    • 0027388316 scopus 로고
    • Coefficients for estimating SAR from soil pH and EC data and calculating pH from SAR and EC values in salinity models
    • C.W.Robbins. 1993. Coefficients for estimating SAR from soil pH and EC data and calculating pH from SAR and EC values in salinity models. Arid Soil Res Rehab. 7:29–38.
    • (1993) Arid Soil Res Rehab , vol.7 , pp. 29-38
    • Robbins, C.W.1
  • 31
    • 0025586833 scopus 로고
    • Calculating pH from EC and SAR values in salinity models and SAR from soil and bore water pH and EC data
    • C.W.Robbins, W.S.Meyer. 1990. Calculating pH from EC and SAR values in salinity models and SAR from soil and bore water pH and EC data. Aust J Soil Res. 28:1001–1011.
    • (1990) Aust J Soil Res , vol.28 , pp. 1001-1011
    • Robbins, C.W.1    Meyer, W.S.2
  • 32
    • 0033535432 scopus 로고    scopus 로고
    • A non-linear rainfall–runoff model using an artificial neural network
    • N.Sajikumara, B.S.Thandaveswara. 1999. A non-linear rainfall–runoff model using an artificial neural network. J Hydrol. 216:32–55.
    • (1999) J Hydrol , vol.216 , pp. 32-55
    • Sajikumara, N.1    Thandaveswara, B.S.2
  • 33
    • 0032122509 scopus 로고    scopus 로고
    • Neural network analysis for hierarchical prediction of soil hydraulic properties
    • M.G.Schaap, F.J.Leij, M.Van Genuchten. 1998. Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil Sci Soc Am J. 62:847–855.
    • (1998) Soil Sci Soc Am J , vol.62 , pp. 847-855
    • Schaap, M.G.1    Leij, F.J.2    Van Genuchten, M.3
  • 34
    • 68149085899 scopus 로고    scopus 로고
    • Fargo (ND): North Dakota State University, NDSU Extension Service EB 57
    • B.Seelig. 2000. Salinity and sodicity in North Dakota soils. Fargo (ND): North Dakota State University, NDSU Extension Service EB 57.
    • (2000) Salinity and sodicity in North Dakota soils
    • Seelig, B.1
  • 35
    • 84966382579 scopus 로고    scopus 로고
    • Modelling of soil sodium adsorption ratio based on soil electrical conductivity
    • M.Seilsepour, M.Rashidi. 2008a. Modelling of soil sodium adsorption ratio based on soil electrical conductivity. ARPN J Agric Biol Sci. 3:27–31.
    • (2008) ARPN J Agric Biol Sci , vol.3 , pp. 27-31
    • Seilsepour, M.1    Rashidi, M.2
  • 36
    • 58849165115 scopus 로고    scopus 로고
    • Prediction of soil cation exchange capacity based on some soil physical and chemical properties
    • M.Seilsepour, M.Rashidi. 2008b. Prediction of soil cation exchange capacity based on some soil physical and chemical properties. World Appl Sci. 3:200–205.
    • (2008) World Appl Sci , vol.3 , pp. 200-205
    • Seilsepour, M.1    Rashidi, M.2
  • 37
    • 60749118483 scopus 로고    scopus 로고
    • Modelling of soil cation exchange capacity based on soil colloidal matrix
    • M.Seilsepour, M.Rashidi. 2008c. Modelling of soil cation exchange capacity based on soil colloidal matrix. Am-Euras J Agric Environ Sci. 3:365–369.
    • (2008) Am-Euras J Agric Environ Sci , vol.3 , pp. 365-369
    • Seilsepour, M.1    Rashidi, M.2
  • 40
    • 33751569366 scopus 로고    scopus 로고
    • Application of a radical basis function neural network for diagnosis of diabetes mellitus
    • P.Venkatesan, S.Anitha. 2006. Application of a radical basis function neural network for diagnosis of diabetes mellitus. Curr Sci. 91:1195–1199.
    • (2006) Curr Sci , vol.91 , pp. 1195-1199
    • Venkatesan, P.1    Anitha, S.2
  • 41
    • 79151474442 scopus 로고    scopus 로고
    • Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils
    • I.Yilmaz, O.Kaynar. 2011. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst Appl. 38:5958–5966.
    • (2011) Expert Syst Appl , vol.38 , pp. 5958-5966
    • Yilmaz, I.1    Kaynar, O.2


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