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Volumn 6, Issue 2, 2007, Pages 423-431

Artificial neural network estimation of saturated hydraulic conductivity

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BULK DENSITY; DATA PROCESSING; HYDRAULIC CONDUCTIVITY; HYDRAULIC PROPERTY; SOIL PROPERTY; SUBSOIL; TERRAIN; TOPSOIL; TROPICAL SOIL;

EID: 34547870556     PISSN: None     EISSN: 15391663     Source Type: Journal    
DOI: 10.2136/vzj2006.0131     Document Type: Article
Times cited : (62)

References (58)
  • 1
    • 32644442539 scopus 로고    scopus 로고
    • Soil characterization and modeling of spatial distribution of saturated hydraulic conductivity at two sites in the Volta basin of Ghana
    • Cuvillier, Göttingen, Germany
    • Agyare, W.A. 2004. Soil characterization and modeling of spatial distribution of saturated hydraulic conductivity at two sites in the Volta basin of Ghana. Ecology and Development no. 17. Cuvillier, Göttingen, Germany.
    • (2004) Ecology and Development , Issue.17
    • Agyare, W.A.1
  • 3
    • 0003332131 scopus 로고
    • Land qualities in space and time
    • J. Bouma and A.K. Bregt ed, Pudoc, Wageningen, the Netherlands
    • Bouma, J. 1989. Land qualities in space and time. p. 3-13. In J. Bouma and A.K. Bregt (ed.) Land qualities in space and time. Pudoc, Wageningen, the Netherlands.
    • (1989) Land qualities in space and time , pp. 3-13
    • Bouma, J.1
  • 4
    • 0001545693 scopus 로고
    • Transfer functions and threshold values: From soil characteristics to land qualities
    • K.J. Beek, P.A. Burrough, and D.E. McCormack ed, Washington, DC. 27 Apr.-2 May. International Inst. for Aerospace Survey and Earth Sciences, Enschede, the Netherlands
    • Bouma, J., and J.A.J. van Lanen. 1987. Transfer functions and threshold values: From soil characteristics to land qualities. p. 106-111. In K.J. Beek, P.A. Burrough, and D.E. McCormack (ed.) Quantified land evaluation procedures. Proc. of the Int. Workshop on Quantified Land Evaluation Procedures, Washington, DC. 27 Apr.-2 May. International Inst. for Aerospace Survey and Earth Sciences, Enschede, the Netherlands.
    • (1987) Quantified land evaluation procedures. Proc. of the Int. Workshop on Quantified Land Evaluation Procedures , pp. 106-111
    • Bouma, J.1    van Lanen, J.A.J.2
  • 5
    • 77956783132 scopus 로고    scopus 로고
    • Bruand, A. 2004. Preliminary grouping of soils. p. 159-174. In Y. Pachepsky and W.J. Rawls (ed.) Development of pedotransfer functions in soil hydrology. 30. Development in Soil Science. Elsevier, Amsterdam, the Netherlands.
    • Bruand, A. 2004. Preliminary grouping of soils. p. 159-174. In Y. Pachepsky and W.J. Rawls (ed.) Development of pedotransfer functions in soil hydrology. Vol 30. Development in Soil Science. Elsevier, Amsterdam, the Netherlands.
  • 6
    • 84972493978 scopus 로고    scopus 로고
    • Breiman, L. 1994. Comment on Neural networks: A review from a statistical perspective by B. Cheng and D.M. Titterington. Statist. Sci. 9(1):38-42.
    • Breiman, L. 1994. Comment on "Neural networks: A review from a statistical perspective" by B. Cheng and D.M. Titterington. Statist. Sci. 9(1):38-42.
  • 7
    • 0026852344 scopus 로고
    • Neural networks and operations research: An overview
    • Burke, L.I., and J.P. Ignizio. 1992. Neural networks and operations research: An overview. Comput. Oper. Res. 19(3-4):179-189.
    • (1992) Comput. Oper. Res , vol.19 , Issue.3-4 , pp. 179-189
    • Burke, L.I.1    Ignizio, J.P.2
  • 8
    • 0022832362 scopus 로고    scopus 로고
    • Burt, T.P., and D.P. Butcher. 1986. Development of topographic indices for use in semi-distributed hillslope runoff models. p. 1-19. In D. Baltenau and O. Slaymaker (ed.) Geomorphology and Land Management. Zeitschrift für Geomorphologie Suppl. Band 58. Gebrüder Borntraeger, Stuttgart, Germany.
    • Burt, T.P., and D.P. Butcher. 1986. Development of topographic indices for use in semi-distributed hillslope runoff models. p. 1-19. In D. Baltenau and O. Slaymaker (ed.) Geomorphology and Land Management. Zeitschrift für Geomorphologie Suppl. Band 58. Gebrüder Borntraeger, Stuttgart, Germany.
  • 9
    • 84972539015 scopus 로고
    • Neural networks: A review from a statistical perspective
    • Cheng, B., and D.M. Titterington. 1994. Neural networks: A review from a statistical perspective. Statist. Sci. 9(1):2-54.
    • (1994) Statist. Sci , vol.9 , Issue.1 , pp. 2-54
    • Cheng, B.1    Titterington, D.M.2
  • 10
    • 0029733768 scopus 로고    scopus 로고
    • Gradient radial basis function networks for nonlinear and nonstationary time series prediction
    • Chng, E.S., S. Chen, and B. Mulgrew. 1996. Gradient radial basis function networks for nonlinear and nonstationary time series prediction. IEEE Trans. Neur. Networks 7(1):191-194.
    • (1996) IEEE Trans. Neur. Networks , vol.7 , Issue.1 , pp. 191-194
    • Chng, E.S.1    Chen, S.2    Mulgrew, B.3
  • 11
    • 34547888122 scopus 로고    scopus 로고
    • diss. Univ. of Goettingen, Germany. Available at, verified 20 Mar. 2007
    • Conrad, O. 2001. DEM software for digital elevation model. (In German.) Ph.D. diss. Univ. of Goettingen, Germany. Available at http://www.geogr.uni- goettingen.de/pg/saga/digem/download/diplom1.pdf (verified 20 Mar. 2007).
    • (2001) DEM software for digital elevation model. (In German.) Ph.D
    • Conrad, O.1
  • 12
    • 0034749335 scopus 로고    scopus 로고
    • Hydrological modeling using artificial neural networks
    • Dawson, C.W., and R.L. Wilby. 2001. Hydrological modeling using artificial neural networks. Prog. Phys. Geogr. 25:80-108.
    • (2001) Prog. Phys. Geogr , vol.25 , pp. 80-108
    • Dawson, C.W.1    Wilby, R.L.2
  • 13
    • 0039988139 scopus 로고    scopus 로고
    • Time series forecasting with neural networks: A comparative study using the airline data
    • Faraway, J., and C. Chatfield. 1998. Time series forecasting with neural networks: A comparative study using the airline data. Appl. Stat. 47(2):203-209.
    • (1998) Appl. Stat , vol.47 , Issue.2 , pp. 203-209
    • Faraway, J.1    Chatfield, C.2
  • 14
    • 34547869358 scopus 로고    scopus 로고
    • Fortin, V., T.B.M.J. Ouarda, and B. Bobee. 1997. Comment on The use of artificial neural networks for the prediction of water quality parameters by H. R. Maier and G.C. Dandy. Water Resour. Res. 33(10):2423-2424.
    • Fortin, V., T.B.M.J. Ouarda, and B. Bobee. 1997. Comment on "The use of artificial neural networks for the prediction of water quality parameters" by H. R. Maier and G.C. Dandy. Water Resour. Res. 33(10):2423-2424.
  • 15
    • 0025919818 scopus 로고
    • Calculating catchment area with divergent flow based on regular grid
    • Freeman, G.T. 1991. Calculating catchment area with divergent flow based on regular grid. Comput. Geosci. 17:413-422.
    • (1991) Comput. Geosci , vol.17 , pp. 413-422
    • Freeman, G.T.1
  • 18
    • 34547907198 scopus 로고    scopus 로고
    • Golden Software. 1999. Surfer 7.0. Golden Software, Golden, CO.
    • Golden Software. 1999. Surfer 7.0. Golden Software, Golden, CO.
  • 19
    • 0003906994 scopus 로고    scopus 로고
    • 4th ed. Prentice Hall, Upper Saddle River, NJ
    • Greene, W.H. 2000. Economic analysis. 4th ed. Prentice Hall, Upper Saddle River, NJ.
    • (2000) Economic analysis
    • Greene, W.H.1
  • 23
    • 0002821764 scopus 로고
    • Artificial neural network models for forecasting and decision making
    • Hill, T., L. Marquez, M. O'Connor, and W. Remus. 1994. Artificial neural network models for forecasting and decision making. Int. J. Forecasting 10:5-15.
    • (1994) Int. J. Forecasting , vol.10 , pp. 5-15
    • Hill, T.1    Marquez, L.2    O'Connor, M.3    Remus, W.4
  • 25
    • 34547880502 scopus 로고    scopus 로고
    • Lacthermacher, G.., and J.D. Fuller. 1994. Backpropagation in hydrological time series forecasting. p. 229-242. In K.W. Hipel, A.I. McLeod, U.S. Panu, and V.P. Singh (ed.) Backpropagation in hydrological time series forecasting. Kluwer Academic, Dordrecht, the Netherlands.
    • Lacthermacher, G.., and J.D. Fuller. 1994. Backpropagation in hydrological time series forecasting. p. 229-242. In K.W. Hipel, A.I. McLeod, U.S. Panu, and V.P. Singh (ed.) Backpropagation in hydrological time series forecasting. Kluwer Academic, Dordrecht, the Netherlands.
  • 27
    • 0032855262 scopus 로고    scopus 로고
    • Empirical comparison of various methods for training feed-forward neural networks for salinity forecasting
    • Maier, H.R., and G.C. Dandy. 1999. Empirical comparison of various methods for training feed-forward neural networks for salinity forecasting. Water Resour. Res. 35:2591-2596.
    • (1999) Water Resour. Res , vol.35 , pp. 2591-2596
    • Maier, H.R.1    Dandy, G.C.2
  • 28
    • 0035108726 scopus 로고    scopus 로고
    • Neural network based modeling of environmental variables: A systematic approach
    • Maier, H.R., and G.C. Dandy. 2001. Neural network based modeling of environmental variables: A systematic approach. Math. Comput. Model. 33:669-682.
    • (2001) Math. Comput. Model , vol.33 , pp. 669-682
    • Maier, H.R.1    Dandy, G.C.2
  • 30
    • 34547862891 scopus 로고    scopus 로고
    • Mathsoft. 1999. S-Plus 2000 Professional Release 2. Mathsoft, Inc, Needham, MA
    • Mathsoft. 1999. S-Plus 2000 Professional Release 2. Mathsoft, Inc., Needham, MA.
  • 31
    • 0036124445 scopus 로고    scopus 로고
    • The neuro-m method for fitting neural network parametric pedotransfer functions
    • Minasny, B., and A. McBratney. 2002. The neuro-m method for fitting neural network parametric pedotransfer functions. Soil Sci. Soc. Am. J. 66:352-361.
    • (2002) Soil Sci. Soc. Am. J , vol.66 , pp. 352-361
    • Minasny, B.1    McBratney, A.2
  • 32
    • 0030159380 scopus 로고    scopus 로고
    • Artificial neural network as rainfall-runoff models
    • Minns, A.W., and M.J. Hall. 1996. Artificial neural network as rainfall-runoff models. Hydrol Sci. J. 41(3):399-417.
    • (1996) Hydrol Sci. J , vol.41 , Issue.3 , pp. 399-417
    • Minns, A.W.1    Hall, M.J.2
  • 34
    • 0030473352 scopus 로고    scopus 로고
    • Artificial neural networks to estimate soil water retention from easily measurably data
    • Pachepsky, Ya., D. Timlin, and G. Varallyay. 1996. Artificial neural networks to estimate soil water retention from easily measurably data. Soil Sci. Soc. Am. J. 60:727-773.
    • (1996) Soil Sci. Soc. Am. J , vol.60 , pp. 727-773
    • Pachepsky, Y.1    Timlin, D.2    Varallyay, G.3
  • 35
    • 77956790215 scopus 로고    scopus 로고
    • Pachepsky, Y., and M.G. Schaap. 2004. Data mining and exploration techniques. p. 21-32. In Y. Pachepsky and W.J. Rawls (ed.) Development of pedotransfer functions in soil hydrology. 30. Development in Soil Science. Elsevier, Amsterdam, the Netherlands.
    • Pachepsky, Y., and M.G. Schaap. 2004. Data mining and exploration techniques. p. 21-32. In Y. Pachepsky and W.J. Rawls (ed.) Development of pedotransfer functions in soil hydrology. Vol 30. Development in Soil Science. Elsevier, Amsterdam, the Netherlands.
  • 36
    • 0036065349 scopus 로고    scopus 로고
    • Environmental correlation of three-dimensional spatial soil variability: A comparison of three adaptive techniques
    • Park, S.J., and P.L.G. Vlek. 2002. Environmental correlation of three-dimensional spatial soil variability: A comparison of three adaptive techniques. Geoderma 109:117-140.
    • (2002) Geoderma , vol.109 , pp. 117-140
    • Park, S.J.1    Vlek, P.L.G.2
  • 39
    • 77956721709 scopus 로고    scopus 로고
    • Romano, N., and G.B. Chirico. 2004. The role of terrain analysis in using and developing pedotransfer functions. p. 273-294. In Y. Pachepsky and W.J. Rawls (ed.) Development of pedotransfer functions in soil hydrology. 30. Development in Soil Science. Elsevier, Amsterdam, the Netherlands.
    • Romano, N., and G.B. Chirico. 2004. The role of terrain analysis in using and developing pedotransfer functions. p. 273-294. In Y. Pachepsky and W.J. Rawls (ed.) Development of pedotransfer functions in soil hydrology. Vol 30. Development in Soil Science. Elsevier, Amsterdam, the Netherlands.
  • 40
    • 0037199709 scopus 로고    scopus 로고
    • Prediction of soil water using soil physical data and terrain attributes
    • Romano, N., and P. Palladino. 2002. Prediction of soil water using soil physical data and terrain attributes. J. Hydrol. 265:56-75.
    • (2002) J. Hydrol , vol.265 , pp. 56-75
    • Romano, N.1    Palladino, P.2
  • 42
    • 0029660470 scopus 로고    scopus 로고
    • Modeling water retention curves of sandy soils using neural networks
    • Schaap, M.G., and W. Bouten. 1996. Modeling water retention curves of sandy soils using neural networks. Water Resour. Res. 32(10):3033-3040.
    • (1996) Water Resour. Res , vol.32 , Issue.10 , pp. 3033-3040
    • Schaap, M.G.1    Bouten, W.2
  • 43
    • 0031783722 scopus 로고    scopus 로고
    • Database-related accuracy and uncertainty of pedotransfer fucntions
    • Schaap, M.G., and F.J. Leij. 1998. Database-related accuracy and uncertainty of pedotransfer fucntions. Soil Sci. 163(10):765-779.
    • (1998) Soil Sci , vol.163 , Issue.10 , pp. 765-779
    • Schaap, M.G.1    Leij, F.J.2
  • 45
    • 0033667949 scopus 로고    scopus 로고
    • Transcending scales of space and time in impact studies of climate and climate change on agro-hydrological responses
    • Schulze, R. 2000. Transcending scales of space and time in impact studies of climate and climate change on agro-hydrological responses. Agric. Ecosyst. Environ. 82:185-212.
    • (2000) Agric. Ecosyst. Environ , vol.82 , pp. 185-212
    • Schulze, R.1
  • 46
    • 33748576907 scopus 로고    scopus 로고
    • Including topography and vegetation attributes for developing pedotransfer functions
    • Sharma, S.K., B.P. Mohanty, and J. Zhu. 2006. Including topography and vegetation attributes for developing pedotransfer functions. Soil Sci. Soc. Am. J. 70:1430-1440.
    • (2006) Soil Sci. Soc. Am. J , vol.70 , pp. 1430-1440
    • Sharma, S.K.1    Mohanty, B.P.2    Zhu, J.3
  • 47
    • 34547886991 scopus 로고    scopus 로고
    • SPSS. 1999. SPSS base 10.0 for Windows user's guide. SPSS, Chicago IL.
    • SPSS. 1999. SPSS base 10.0 for Windows user's guide. SPSS, Chicago IL.
  • 48
    • 34547871400 scopus 로고    scopus 로고
    • SRI, Soil Research Institute, Kumasi, Ghana
    • SRI. 1999. FAO soil map of Ghana. Soil Research Institute, Kumasi, Ghana.
    • (1999) FAO soil map of Ghana
  • 49
    • 77956739130 scopus 로고    scopus 로고
    • Tomasella, J., and M. Hodnett. 2004. Pedotransfer functions for tropical soils. p. 415-435. In Y. Pachepsky and W.J. Rawls (ed.) Development of pedotransfer functions in soil hydrology. 30. Development in Soil Science. Elsevier, Amsterdam, the Netherlands.
    • Tomasella, J., and M. Hodnett. 2004. Pedotransfer functions for tropical soils. p. 415-435. In Y. Pachepsky and W.J. Rawls (ed.) Development of pedotransfer functions in soil hydrology. Vol 30. Development in Soil Science. Elsevier, Amsterdam, the Netherlands.
  • 50
    • 0009454128 scopus 로고    scopus 로고
    • Tibshirani, R. 1994. Comment on 'Neural networks: A review from statistical. perspective' by B. Cheng and D.M. Titterington. Statist. Sci. 9(1):48-49.
    • Tibshirani, R. 1994. Comment on 'Neural networks: A review from statistical. perspective' by B. Cheng and D.M. Titterington. Statist. Sci. 9(1):48-49.
  • 52
    • 0001858142 scopus 로고
    • Spatial variability of soil physical properties in the field
    • D. Hillel ed, Academic Press, New York
    • Warrick, A.W., and D.R. Nielson. 1980. Spatial variability of soil physical properties in the field. p. 319-344. In D. Hillel (ed.) Applications of soil physics. Academic Press, New York.
    • (1980) Applications of soil physics , pp. 319-344
    • Warrick, A.W.1    Nielson, D.R.2
  • 53
    • 0344224257 scopus 로고    scopus 로고
    • On the spatial scaling of soil moisture
    • Western, A.W., and G. Blöschl. 1999. On the spatial scaling of soil moisture. J. Hydrol. 217:203-224.
    • (1999) J. Hydrol , vol.217 , pp. 203-224
    • Western, A.W.1    Blöschl, G.2
  • 54
    • 0000243355 scopus 로고
    • Learning in artificial neural networks: A statistical perspective
    • White, H. 1989. Learning in artificial neural networks: A statistical perspective. Neural Comput. 1:425-464.
    • (1989) Neural Comput , vol.1 , pp. 425-464
    • White, H.1
  • 55
    • 0001856275 scopus 로고
    • Spatial variability: Its documentation, accommodation, and implication to soil surveys
    • D.R. Nielsen and J. Bouma ed, Pudoc, Wageningen, the Netherlands
    • Wilding, L.P. 1984. Spatial variability: Its documentation, accommodation, and implication to soil surveys. p. 166-193. In D.R. Nielsen and J. Bouma (ed.) Soil spatial variability. Pudoc, Wageningen, the Netherlands.
    • (1984) Soil spatial variability , pp. 166-193
    • Wilding, L.P.1
  • 56
    • 0002003252 scopus 로고    scopus 로고
    • Digital terrain Analysis
    • J.P. Wilson and J.C. Gallant ed, John Wiley & Sons, New York
    • Wilson, J.P., and J.C. Gallant. 2000. Digital terrain Analysis. p. 1-27. In J.P. Wilson and J.C. Gallant (ed.) Terrain analysis: Principles and application. John Wiley & Sons, New York.
    • (2000) Terrain analysis: Principles and application , pp. 1-27
    • Wilson, J.P.1    Gallant, J.C.2
  • 57
    • 0031127257 scopus 로고    scopus 로고
    • Efficient backpropagation learning using optimal learning rate and momentum
    • Yu, X.H., and G.A. Chen. 1997. Efficient backpropagation learning using optimal learning rate and momentum. Neural Netw. 10:517-527.
    • (1997) Neural Netw , vol.10 , pp. 517-527
    • Yu, X.H.1    Chen, G.A.2


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