Abstract
Modeling unsaturated water flow in soil requires knowledge of the hydraulic properties of soil. However correlation among soil hydraulic properties such as the relationship between saturated soil water content θ s and saturated soil hydraulic conductivity Ks as function of soil depth is in stochastic pattern. On the other hand, soil-water profile process is also believed to be highly nonlinear, time-varying, spatially distributed, and not easily described by simple models. In this study, the potential of implementing Artificial Neural Networks ANN model was proposed and investigated to map the soil-water profile in terms of Ks and θ s with respect to the soil depth d. Site experimental data sets on the hydraulic properties of weathered granite soils were collected. These data sets include the observed values of saturated and unsaturated hydraulic conductivities, saturated water contents, and retention curves.The proposed ANN model was examined utilizing 49 records data collected from field experiments. The results showed that the ANN model has the ability to detect and extract the stochastic behaviour of the water in the soil and draw the water-soil profile with relatively high accuracy.
| Original language | English |
|---|---|
| Title of host publication | Unsaturated Soils - Proceedings of the 5th International Conference on Unsaturated Soils |
| Pages | 887-893 |
| Number of pages | 7 |
| Volume | 2 |
| Publication status | Published - 2011 |
| Event | 5th International Conference on Unsaturated Soils - Barcelona Duration: 6 Sep 2010 → 8 Sep 2010 |
Other
| Other | 5th International Conference on Unsaturated Soils |
|---|---|
| City | Barcelona |
| Period | 6/9/10 → 8/9/10 |
Fingerprint
ASJC Scopus subject areas
- Soil Science
Cite this
Mapping soil-water profile utilizing non-linear neural network based model. / Mukhlisin, Muhammad; El-Shafie, Ahmed; Taha, Mohd. Raihan.
Unsaturated Soils - Proceedings of the 5th International Conference on Unsaturated Soils. Vol. 2 2011. p. 887-893.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Mapping soil-water profile utilizing non-linear neural network based model
AU - Mukhlisin, Muhammad
AU - El-Shafie, Ahmed
AU - Taha, Mohd. Raihan
PY - 2011
Y1 - 2011
N2 - Modeling unsaturated water flow in soil requires knowledge of the hydraulic properties of soil. However correlation among soil hydraulic properties such as the relationship between saturated soil water content θ s and saturated soil hydraulic conductivity Ks as function of soil depth is in stochastic pattern. On the other hand, soil-water profile process is also believed to be highly nonlinear, time-varying, spatially distributed, and not easily described by simple models. In this study, the potential of implementing Artificial Neural Networks ANN model was proposed and investigated to map the soil-water profile in terms of Ks and θ s with respect to the soil depth d. Site experimental data sets on the hydraulic properties of weathered granite soils were collected. These data sets include the observed values of saturated and unsaturated hydraulic conductivities, saturated water contents, and retention curves.The proposed ANN model was examined utilizing 49 records data collected from field experiments. The results showed that the ANN model has the ability to detect and extract the stochastic behaviour of the water in the soil and draw the water-soil profile with relatively high accuracy.
AB - Modeling unsaturated water flow in soil requires knowledge of the hydraulic properties of soil. However correlation among soil hydraulic properties such as the relationship between saturated soil water content θ s and saturated soil hydraulic conductivity Ks as function of soil depth is in stochastic pattern. On the other hand, soil-water profile process is also believed to be highly nonlinear, time-varying, spatially distributed, and not easily described by simple models. In this study, the potential of implementing Artificial Neural Networks ANN model was proposed and investigated to map the soil-water profile in terms of Ks and θ s with respect to the soil depth d. Site experimental data sets on the hydraulic properties of weathered granite soils were collected. These data sets include the observed values of saturated and unsaturated hydraulic conductivities, saturated water contents, and retention curves.The proposed ANN model was examined utilizing 49 records data collected from field experiments. The results showed that the ANN model has the ability to detect and extract the stochastic behaviour of the water in the soil and draw the water-soil profile with relatively high accuracy.
UR - http://www.scopus.com/inward/record.url?scp=84859972813&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84859972813&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9780415604307
VL - 2
SP - 887
EP - 893
BT - Unsaturated Soils - Proceedings of the 5th International Conference on Unsaturated Soils
ER -