SSSAJ
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Published online 20 September 2006
Published in Soil Sci Soc Am J 70:1851-1859 (2006)
DOI: 10.2136/sssaj2006.0045
© 2006 Soil Science Society of America
677 S. Segoe Rd., Madison, WI 53711 USA
This Article
Right arrow Abstract Freely available
Right arrow Full Text Free
Right arrow Full Text (PDF) Free
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Web of Science (3)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Parasuraman, K.
Right arrow Articles by Si, B. C.
Right arrow Search for Related Content
PubMed
Right arrow Articles by Parasuraman, K.
Right arrow Articles by Si, B. C.
GeoRef
Right arrow GeoRef Citation
Agricola
Right arrow Articles by Parasuraman, K.
Right arrow Articles by Si, B. C.
Related Collections
Right arrow Soil Hydrology
Right arrow Case Studies
Right arrow Soil Physics

Estimating Saturated Hydraulic Conductivity In Spatially Variable Fields Using Neural Network Ensembles

Kamban Parasuramana, Amin Elshorbagya,* and Bing Cheng Sib

a Centre for Advanced Numerical Simulation (CANSIM), Dep. of Civil & Geological Engineering, Univ. of Saskatchewan, Saskatoon, SK, Canada
b Dep. of Soil Science, Univ. of Saskatchewan, Saskatoon, SK, Canada


Figure 1
View larger version (19K):

[in a new window]
  		Fig. 1. Structure of the three-layered feed-forward neural network (FF-NN).

 

Figure 2
View larger version (15K):

[in a new window]
  		Fig. 2. Scatter plots between the measured and the computed Ks by (a) Rosetta; (b) Field(Bagging); and (c) Field(Boosting) for Smeaton with SSC as Inputs. The ‘solid’ points represent the training instances and the ‘open triangular’ points represent the testing instances.

 

Figure 3
View larger version (15K):

[in a new window]
  		Fig. 3. Scatter plots between the measured and the computed Ks by (a) Rosetta; (b) Field(Bagging); and (c) Field(Boosting) for Smeaton with SSC and {rho}b as Inputs. The ‘solid’ points represent the training instances and the ‘open triangular’ points represent the testing instances.

 

Figure 4
View larger version (12K):

[in a new window]
  		Fig. 4. Scatter plots between the measured and the computed Ks by (a) Rosetta; (b) Field(Bagging); and (c) Field(Boosting) for Alvena with SSC as Inputs. The ‘solid’ points represent the training instances and the ‘open triangular’ points represent the testing instances.

 

Figure 5
View larger version (12K):

[in a new window]
  		Fig. 5. Scatter plots between the measured and the computed Ks by (a) Rosetta; (b) Field(Bagging); and (c) Field(Boosting) for Alvena with SSC and {rho}b as Inputs. The ‘solid’ points represent the training instances and the ‘open triangular’ points represent the testing instances.

 




HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
The SCI Journals Agronomy Journal Crop Science
Journal of Natural Resources
and Life Sciences Education		 Vadose Zone Journal
Journal of Plant Registrations Journal of
Environmental Quality		 The Plant Genome
Copyright © 2006 by the Soil Science Society of America.