In Search of a Soil Moisture Content Simulation Model: Mechanistic and Data Mining Approach Based on TDR Method Results
Soil moisture content simulation models have continuously been an important research objective. In particular, the comparisons of the performance of different model types deserve proper attention. Therefore, the quality of selected physically-based and statistical models was analyzed utilizing the d...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/20/6819 |
| _version_ | 1797513175135944704 |
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| author | Andrzej Brandyk Bartosz Szeląg Adam Kiczko Marcin Krukowski Adam Kozioł Jerzy Piotrowski Grzegorz Majewski |
| author_facet | Andrzej Brandyk Bartosz Szeląg Adam Kiczko Marcin Krukowski Adam Kozioł Jerzy Piotrowski Grzegorz Majewski |
| author_sort | Andrzej Brandyk |
| collection | DOAJ |
| description | Soil moisture content simulation models have continuously been an important research objective. In particular, the comparisons of the performance of different model types deserve proper attention. Therefore, the quality of selected physically-based and statistical models was analyzed utilizing the data from the Time Domain Reflectometry technique. An E-Test measurement system was applied with the reflectogram interpreted into soil volumetric moisture content by proper calibration equations. The gathered data facilitated to calibrate the physical model of Deardorff and establish parameters of: support vector machines, multivariate adaptive regression spline, and boosted trees model. The general likelihood uncertainty estimation revealed the sensitivity of individual model parameters. As it was assumed, a simple structure of statistical models was achieved but no direct physical interpretation of their parameters, contrary to a physically-based method. The TDR technique proved useful for the calibration of different soil moisture models and a satisfactory quality for their future exploitation. |
| first_indexed | 2024-03-10T06:12:58Z |
| format | Article |
| id | doaj.art-04770730a7674cc49024a69da19798a0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T06:12:58Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-04770730a7674cc49024a69da19798a02023-11-22T19:57:58ZengMDPI AGSensors1424-82202021-10-012120681910.3390/s21206819In Search of a Soil Moisture Content Simulation Model: Mechanistic and Data Mining Approach Based on TDR Method ResultsAndrzej Brandyk0Bartosz Szeląg1Adam Kiczko2Marcin Krukowski3Adam Kozioł4Jerzy Piotrowski5Grzegorz Majewski6Water Centre, Warsaw University of Life Sciences, 02-676 Warsaw, PolandDepartment of Geotechnics, Geomatics and Waste Management, Kielce University of Technology, 25-314 Kielce, PolandInstitute of Environmental Engineering, Warsaw University of Life Sciences, 02-787 Warsaw, PolandInstitute of Environmental Engineering, Warsaw University of Life Sciences, 02-787 Warsaw, PolandInstitute of Environmental Engineering, Warsaw University of Life Sciences, 02-787 Warsaw, PolandDepartment of Building Physics and Renewable Energy, Kielce University of Technology, 25-314 Kielce, PolandInstitute of Environmental Engineering, Warsaw University of Life Sciences, 02-787 Warsaw, PolandSoil moisture content simulation models have continuously been an important research objective. In particular, the comparisons of the performance of different model types deserve proper attention. Therefore, the quality of selected physically-based and statistical models was analyzed utilizing the data from the Time Domain Reflectometry technique. An E-Test measurement system was applied with the reflectogram interpreted into soil volumetric moisture content by proper calibration equations. The gathered data facilitated to calibrate the physical model of Deardorff and establish parameters of: support vector machines, multivariate adaptive regression spline, and boosted trees model. The general likelihood uncertainty estimation revealed the sensitivity of individual model parameters. As it was assumed, a simple structure of statistical models was achieved but no direct physical interpretation of their parameters, contrary to a physically-based method. The TDR technique proved useful for the calibration of different soil moisture models and a satisfactory quality for their future exploitation.https://www.mdpi.com/1424-8220/21/20/6819soil moisture contentdielectric permittivitymodelinguncertaintysensitivity analysis |
| spellingShingle | Andrzej Brandyk Bartosz Szeląg Adam Kiczko Marcin Krukowski Adam Kozioł Jerzy Piotrowski Grzegorz Majewski In Search of a Soil Moisture Content Simulation Model: Mechanistic and Data Mining Approach Based on TDR Method Results Sensors soil moisture content dielectric permittivity modeling uncertainty sensitivity analysis |
| title | In Search of a Soil Moisture Content Simulation Model: Mechanistic and Data Mining Approach Based on TDR Method Results |
| title_full | In Search of a Soil Moisture Content Simulation Model: Mechanistic and Data Mining Approach Based on TDR Method Results |
| title_fullStr | In Search of a Soil Moisture Content Simulation Model: Mechanistic and Data Mining Approach Based on TDR Method Results |
| title_full_unstemmed | In Search of a Soil Moisture Content Simulation Model: Mechanistic and Data Mining Approach Based on TDR Method Results |
| title_short | In Search of a Soil Moisture Content Simulation Model: Mechanistic and Data Mining Approach Based on TDR Method Results |
| title_sort | in search of a soil moisture content simulation model mechanistic and data mining approach based on tdr method results |
| topic | soil moisture content dielectric permittivity modeling uncertainty sensitivity analysis |
| url | https://www.mdpi.com/1424-8220/21/20/6819 |
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