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

This Article Abstract 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 ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (9) Citing Articles via Google Scholar Google Scholar Articles by Minasny, B. Articles by Denton, M. A. Search for Related Content PubMed Articles by Minasny, B. Articles by Denton, M. A. GeoRef GeoRef Citation Agricola Articles by Minasny, B. Articles by Denton, M. A. Related Collections Soil Physics Pedotransfer Functions Water Retention/Capillary Pressure Water Flow Models Hydraulic Conductivity

Neural Networks Prediction of Soil Hydraulic Functions for Alluvial Soils Using Multistep Outflow Data

^a Faculty of Agriculture, Food, and Natural Resources, McMillan Building A05, The Univ. of Sydney, NSW 2006, Australia
^b Hydrology Program, Dep. of Land, Air, and Water Resources, 123 Veihmeyer Hall, Univ. of California, Davis, CA 95616
^c Dep. of Water Resources, Water Use Efficiency Office, 901 P Street, Third Floor, P.O. Box 942836, Sacramento, CA 94236-0001
^d Dep. of Environmental Sci., 2217 Geology Building, Univ. of California, Riverside, CA 92521

View larger version (44K): [in a new window]   Fig. 1. Particle size distribution of the training dataset. To determine soil type, find intersection of lines, parallel to main coordinate axes.

View larger version (61K): [in a new window]   Fig. 2. (a) Soil-water retention, and (b) unsaturated hydraulic conductivity curves of the training dataset, making distinction between Long Term Research on Agricultural Systems (LTRAS), Kearney, and Diener datasets. , water content; h, soil-water matric head; K, hydraulic conductivity.

View larger version (26K): [in a new window]   Fig. 3. Measured vs. predicted (a) water retention and (b) unsaturated hydraulic conductivity data when five input parameters are used (sand, silt, clay, bulk density, and saturated water content). , water content; K, hydraulic conductivity.

View larger version (18K): [in a new window]   Fig. 4. Examples of measured (dots) and predicted (solid line) soil hydraulic functions with five input parameters. The dashed lines span the 95% confidence interval of the predictions. The comparison is presented for (a) Long Term Research on Agricultural Systems (LTRAS) silt loam, (b) Kearney sand, and (c) Diener loam. , water content; h, soil-water matric head; K, hydraulic conductivity; RMSR, root mean squares of residuals.

View larger version (35K): [in a new window]   Fig. 5. Box plots of RMSR values for different neural network models predicting (a) water retention, and (b) unsaturated hydraulic conductivity. The box plots summarize the distribution of root mean squares of residuals (RMSR). The horizontal line in each box signifies the median value, whereas top and bottom of the box represent the 25th and 75th quantiles. The whiskers extend from the ends of the box to the outermost data point that falls within the distances of upper quartile + 1.5 (interquartile range), and lower quartile – 1.5 (interquartile range), respectively. , water content; K, hydraulic conductivity.

View larger version (37K): [in a new window]   Fig. 6. Sensitivity analysis of various input data (horizontal axis) to soil hydraulic parameters (vertical axis). –100, water retention at a matric head value of –100 cm; r, residual water content; s, saturated water content; b, bulk density; K, hydraulic conductivity.