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Utilisation de réseaux de neurones pour l'étalonnage de mesures par réflectométrie en domaine temporel

Persson, Magnus LU ; Berndtsson, Ronny LU orcid and Sivakumar, Bellie (2001) In Hydrological Sciences Journal 46(3). p.389-398
Abstract

Time-domain reflectometry (TDR) is an electromagnetic technique for measurements of water and solute transport in soils. The relationship between the TDR-measured dielectric constant (Ka) and bulk soil electrical conductivity ([sgrave]a) to water content (θW) and solute concentration is difficult to describe physically due to the complex dielectric response of wet soil. This has led to the development of mostly empirical calibration models. In the present study, artificial neural networks (ANNs) are utilized for calculations of θw and soil solution electrical conductivity ([sgrave]w) from TDR-measured Ka and [sgrave]a in sand. The ANN model performance is... (More)

Time-domain reflectometry (TDR) is an electromagnetic technique for measurements of water and solute transport in soils. The relationship between the TDR-measured dielectric constant (Ka) and bulk soil electrical conductivity ([sgrave]a) to water content (θW) and solute concentration is difficult to describe physically due to the complex dielectric response of wet soil. This has led to the development of mostly empirical calibration models. In the present study, artificial neural networks (ANNs) are utilized for calculations of θw and soil solution electrical conductivity ([sgrave]w) from TDR-measured Ka and [sgrave]a in sand. The ANN model performance is compared to other existing models. The results show that the ANN performs consistently better than all other models, suggesting the suitability of ANNs for accurate TDR calibrations.

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Please use this url to cite or link to this publication:
author
; and
organization
alternative title
Using neural networks for calibration of time-domain reflectometry measurements
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Electrical conductivity, Neural networks, Soil water content, Time-domain reflectometry
in
Hydrological Sciences Journal
volume
46
issue
3
pages
10 pages
publisher
Taylor & Francis
external identifiers
  • scopus:18844467644
ISSN
0262-6667
DOI
10.1080/02626660109492834
language
French
LU publication?
yes
id
a7e4fda2-1197-4855-abc5-b31037f54644
date added to LUP
2023-08-17 14:59:52
date last changed
2023-10-02 13:15:32
@article{a7e4fda2-1197-4855-abc5-b31037f54644,
  abstract     = {{<p>Time-domain reflectometry (TDR) is an electromagnetic technique for measurements of water and solute transport in soils. The relationship between the TDR-measured dielectric constant (K<sub>a</sub>) and bulk soil electrical conductivity ([sgrave]<sub>a</sub>) to water content (θ<sub>W</sub>) and solute concentration is difficult to describe physically due to the complex dielectric response of wet soil. This has led to the development of mostly empirical calibration models. In the present study, artificial neural networks (ANNs) are utilized for calculations of θ<sub>w</sub> and soil solution electrical conductivity ([sgrave]<sub>w</sub>) from TDR-measured K<sub>a</sub> and [sgrave]<sub>a</sub> in sand. The ANN model performance is compared to other existing models. The results show that the ANN performs consistently better than all other models, suggesting the suitability of ANNs for accurate TDR calibrations.</p>}},
  author       = {{Persson, Magnus and Berndtsson, Ronny and Sivakumar, Bellie}},
  issn         = {{0262-6667}},
  keywords     = {{Electrical conductivity; Neural networks; Soil water content; Time-domain reflectometry}},
  language     = {{fre}},
  number       = {{3}},
  pages        = {{389--398}},
  publisher    = {{Taylor & Francis}},
  series       = {{Hydrological Sciences Journal}},
  title        = {{Utilisation de réseaux de neurones pour l'étalonnage de mesures par réflectométrie en domaine temporel}},
  url          = {{http://dx.doi.org/10.1080/02626660109492834}},
  doi          = {{10.1080/02626660109492834}},
  volume       = {{46}},
  year         = {{2001}},
}