HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (3) Citing Articles via Google Scholar Google Scholar Articles by Parasuraman, K. Articles by Si, B. C. Search for Related Content PubMed Articles by Parasuraman, K. Articles by Si, B. C. GeoRef GeoRef Citation Agricola Articles by Parasuraman, K. Articles by Si, B. C. Related Collections Soil Hydrology Case Studies Soil Physics

Soil Physics

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

^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

^* Corresponding author (Amin.Elshorbagy{at}usask.ca)

Modeling contaminant and water flow through soil requires accurate estimates of soil hydraulic properties in field scale. Although artificial neural networks (ANNs) based pedotransfer functions (PTFs) have been successfully adopted in modeling soil hydraulic properties at larger scales (national, continental, and intercontinental), the utility of ANNs in modeling saturated hydraulic conductivity (K_s) at a smaller (field) scale has rarely been reported. Hence, the objectives of this study are (i) to investigate the applicability of neural networks in estimating K_s at field scales, (ii) to compare the performance of the field-scale PTFs with the published neural networks program Rosetta, and (iii) to compare the performance of two different ensemble methods, namely Bagging and Boosting in estimating K_s. Datasets from two distinct sites are considered in the study. The performances of the models were evaluated when only sand, silt, and clay content (SSC) were used as inputs, and when SSC and bulk density _b (SSC+ _b) were used as inputs. For both datasets, the field scale models performed better than Rosetta. The comparison of field-scale ANN models employing bagging and boosting algorithms indicates that the neural network model employing the boosting algorithm results in better generalization by reducing both the bias and variance of the neural network models.

Abbreviations: ANNs, artificial neural networks • _b, bulk density • FF-NN, feed-forward neural network • MR, mean residual • MRE, mean relative error • MSE, mean sum of squares of the network errors • PTF, pedotransfer function • RMSE, root mean squared error • SSC, sand, silt, and clay

This article has been cited by other articles:

				L. Cockx, M. Van Meirvenne, U. W. A. Vitharana, L. P. C. Verbeke, D. Simpson, T. Saey, and F. M. B. Van Coillie Extracting Topsoil Information from EM38DD Sensor Data using a Neural Network Approach Soil Sci. Soc. Am. J., October 21, 2009; 73(6): 2051 - 2058. [Abstract] [Full Text] [PDF]