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| dc.contributor | Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa |
|---|---|
| dc.contributor | Universitat Politècnica de Catalunya. ADBD - Anàlisi de Dades Complexes per a les Decisions Empresarials |
| dc.contributor.author | Aljoumani, Basem |
| dc.contributor.author | Sánchez Espigares, Josep Anton |
| dc.contributor.author | Wessolek, Gerd |
| dc.date | 2018-12-13 |
| dc.identifier.citation | Aljoumani, B.; Sanchez-Espigares, J.; Wessolek, G. Estimating pore water electrical conductivity of sandy soil from time domain reflectometry records using a time-varying dynamic linear model. "Sensors", 13 Desembre 2018, vol. 18, núm. 12, p. 4403-4414. |
| dc.identifier.citation | 1424-8220 |
| dc.identifier.citation | 10.3390/s18124403 |
| dc.identifier.uri | http://hdl.handle.net/2117/127845 |
| dc.description.abstract | Despite the importance of computing soil pore water electrical conductivity (sp) from soil bulk electrical conductivity (sb) in ecological and hydrological applications, a good method of doing so remains elusive. The Hilhorst concept offers a theoretical model describing a linear relationship between sb, and relative dielectric permittivity (eb) in moist soil. The reciprocal of pore water electrical conductivity (1/sp) appears as a slope of the Hilhorst model and the ordinary least squares (OLS) of this linear relationship yields a single estimate ( 1/spˆ ) of the regression parameter vector (sp) for the entire data. This study was carried out on a sandy soil under laboratory conditions. We used a time-varying dynamic linear model (DLM) and the Kalman filter (Kf) to estimate the evolution of sp over time. A time series of the relative dielectric permittivity (eb) and sb of the soil were measured using time domain reflectometry (TDR) at different depths in a soil column to transform the deterministic Hilhorst model into a stochastic model and evaluate the linear relationship between eb and sb in order to capture deterministic changes to (1/sp). Applying the Hilhorst model, strong positive autocorrelations between the residuals could be found. By using and modifying them to DLM, the observed and modeled data of eb obtain a much better match and the estimated evolution of sp converged to its true value. Moreover, the offset of this linear relation varies for each soil depth |
| dc.description.abstract | Peer Reviewed |
| dc.language.iso | eng |
| dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) |
| dc.relation | https://www.mdpi.com/1424-8220/18/12/4403 |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
| dc.rights | info:eu-repo/semantics/openAccess |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
| dc.subject | Àrees temàtiques de la UPC::Enginyeria elèctrica |
| dc.subject | Electric conductivity |
| dc.subject | Kalman filtering |
| dc.subject | Electrical conductivity |
| dc.subject | relative dielectric permittivity |
| dc.subject | time domain reflectometry |
| dc.subject | kalman filter |
| dc.subject | dynamic linear model |
| dc.subject | Conductivitat elèctrica |
| dc.subject | Kalman, Filtratge de |
| dc.title | Estimating pore water electrical conductivity of sandy soil from time domain reflectometry records using a time-varying dynamic linear model |
| dc.type | info:eu-repo/semantics/submittedVersion |
| dc.type | info:eu-repo/semantics/article |
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