Evaluation and development of pedotransfer functions and artificial neural networks to saturation moisture content estimation
Modeling of irrigation and agricultural drainage requires knowledge of the soil hydraulic properties. However, uncertainty in the direct measurement of the saturation moisture content (θs) has been generated in several methodologies for its estimation, such as Pedotransfer Functions (PTFs) and Artif...
| Autores principales: | , , , |
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| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
Multidisciplinary Digital Publishing Institute
2023
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| Acceso en línea: | http://eprints.uanl.mx/29919/7/29919.pdf |
| _version_ | 1838551026429329408 |
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| author | Trejo Alonso, Josué Fuentes, Sebastián Morales Durán, Nami Chávez, Carlos |
| author_facet | Trejo Alonso, Josué Fuentes, Sebastián Morales Durán, Nami Chávez, Carlos |
| author_sort | Trejo Alonso, Josué |
| collection | Repositorio Institucional |
| description | Modeling of irrigation and agricultural drainage requires knowledge of the soil hydraulic properties. However, uncertainty in the direct measurement of the saturation moisture content (θs) 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 (θs) estimation, which relate to the proportion of sand and clay, bulk density (BD) as well as the saturated hydraulic conductivity (Ks). In addition, ANNs were developed with different combinations of input and hidden layers for the estimation of θs. The results showed R2 values from 0.9046 ≤ R
2 ≤ 0.9877 for the eight different PTFs, while with the ANNs, values of R2 > 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 Ks), accurate estimate of θs is obtained. With the development of these models (PTFs and ANNs), high R2 values were obtained for 10 of the 12 textural classes. |
| format | Article |
| id | eprints-29919 |
| institution | UANL |
| language | English |
| publishDate | 2023 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | eprints |
| spelling | eprints-299192025-07-03T16:09:43Z http://eprints.uanl.mx/29919/ Evaluation and development of pedotransfer functions and artificial neural networks to saturation moisture content estimation Trejo Alonso, Josué Fuentes, Sebastián Morales Durán, Nami Chávez, Carlos Modeling of irrigation and agricultural drainage requires knowledge of the soil hydraulic properties. However, uncertainty in the direct measurement of the saturation moisture content (θs) 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 (θs) estimation, which relate to the proportion of sand and clay, bulk density (BD) as well as the saturated hydraulic conductivity (Ks). In addition, ANNs were developed with different combinations of input and hidden layers for the estimation of θs. The results showed R2 values from 0.9046 ≤ R 2 ≤ 0.9877 for the eight different PTFs, while with the ANNs, values of R2 > 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 Ks), accurate estimate of θs is obtained. With the development of these models (PTFs and ANNs), high R2 values were obtained for 10 of the 12 textural classes. Multidisciplinary Digital Publishing Institute 2023-01-04 Article PeerReviewed text en cc_by_nc_nd http://eprints.uanl.mx/29919/7/29919.pdf http://eprints.uanl.mx/29919/7.haspreviewThumbnailVersion/29919.pdf Trejo Alonso, Josué y Fuentes, Sebastián y Morales Durán, Nami y Chávez, Carlos (2023) Evaluation and development of pedotransfer functions and artificial neural networks to saturation moisture content estimation. Water, 15 (2). p. 220. ISSN 2073-4441 http://doi.org/10.3390/w15020220 doi:10.3390/w15020220 |
| spellingShingle | Trejo Alonso, Josué Fuentes, Sebastián Morales Durán, Nami Chávez, Carlos Evaluation and development of pedotransfer functions and artificial neural networks to saturation moisture content estimation |
| thumbnail | https://rediab.uanl.mx/themes/sandal5/images/online.png |
| title | Evaluation and development of pedotransfer functions and artificial neural networks to saturation moisture content estimation |
| title_full | Evaluation and development of pedotransfer functions and artificial neural networks to saturation moisture content estimation |
| title_fullStr | Evaluation and development of pedotransfer functions and artificial neural networks to saturation moisture content estimation |
| title_full_unstemmed | Evaluation and development of pedotransfer functions and artificial neural networks to saturation moisture content estimation |
| title_short | Evaluation and development of pedotransfer functions and artificial neural networks to saturation moisture content estimation |
| title_sort | evaluation and development of pedotransfer functions and artificial neural networks to saturation moisture content estimation |
| url | http://eprints.uanl.mx/29919/7/29919.pdf |
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