We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Evaluation and Development of Pedotransfer Functions and Artificial Neural Networks to Saturation Moisture Content Estimation.
- Authors
Trejo-Alonso, Josué; Fuentes, Sebastián; Morales-Durán, Nami; Chávez, Carlos
- Abstract
Modeling of irrigation and agricultural drainage requires knowledge of the soil hydraulic properties. However, uncertainty in the direct measurement of the saturation moisture content ( θ s ) has been generated in several methodologies for its estimation, such as Pedotransfer Functions (PTFs) and Artificial Neuronal Networks (ANNs). In this work, eight different PTFs were developed for the ( θ s ) estimation, which relate to the proportion of sand and clay, bulk density (BD) as well as the saturated hydraulic conductivity ( K s ). In addition, ANNs were developed with different combinations of input and hidden layers for the estimation of θ s . The results showed R 2 values from 0.9046 ≤ R 2 ≤ 0.9877 for the eight different PTFs, while with the ANNs, values of R 2 > 0.9891 were obtained. Finally, the root-mean-square error (RMSE) was obtained for each ANN configuration, with results ranging from 0.0245 ≤ RMSE ≤ 0.0262 . It was found that with particular soil characteristic parameters (% Clay, % Silt, % Sand, BD and K s ), accurate estimate of θ s is obtained. With the development of these models (PTFs and ANNs), high R 2 values were obtained for 10 of the 12 textural classes.
- Subjects
ARTIFICIAL neural networks; HYDRAULIC conductivity; MOISTURE measurement; NEURAL circuitry; MOISTURE
- Publication
Water (20734441), 2023, Vol 15, Issue 2, p220
- ISSN
2073-4441
- Publication type
Article
- DOI
10.3390/w15020220