A framework based on mechanistic modelling and machine learning for soil moisture estimation
Abstract Soil moisture has many important applications in subsurface water movement. Methods for measuring soil moisture are either destructive or require prior calibration. The aim of this study was to provide a framework for estimating soil water content with limited data (rainfall and evaporation...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Discover Soil |
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| Online Access: | https://doi.org/10.1007/s44378-025-00086-9 |
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| author | Sabri Kanzari Sana Ben Mariem Samir Ghannem Safouane Mouelhi Hiba Ghazouani Bechir Ben Nouna |
| author_facet | Sabri Kanzari Sana Ben Mariem Samir Ghannem Safouane Mouelhi Hiba Ghazouani Bechir Ben Nouna |
| author_sort | Sabri Kanzari |
| collection | DOAJ |
| description | Abstract Soil moisture has many important applications in subsurface water movement. Methods for measuring soil moisture are either destructive or require prior calibration. The aim of this study was to provide a framework for estimating soil water content with limited data (rainfall and evaporation). The hydraulic properties of four layers of soil from Tunisia were measured to a depth of 80 cm. These values were used as inputs to the Hydrus 1D model to generate soil water content profiles over a 27-year period. 109 profiles were calculated using machine learning algorithms (regression trees; random forest; support vector machine) to predict soil moisture content from daily values of rainfall and evaporation. The statistical performance indices of prediction, root mean square error and correlation coefficient indicate the superiority of regression trees over other methods for all soil layers and during both calibration and validation processes and reproducing the seasonal variation of soil moisture. |
| format | Article |
| id | doaj-art-a5e549e943aa4ea8a1eb7ece2dbd3975 |
| institution | Kabale University |
| issn | 3005-1223 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Soil |
| spelling | doaj-art-a5e549e943aa4ea8a1eb7ece2dbd39752025-08-20T04:01:42ZengSpringerDiscover Soil3005-12232025-07-012111810.1007/s44378-025-00086-9A framework based on mechanistic modelling and machine learning for soil moisture estimationSabri Kanzari0Sana Ben Mariem1Samir Ghannem2Safouane Mouelhi3Hiba Ghazouani4Bechir Ben Nouna5National research Institute of rural engineering, Water and Forests, University of CarthageNational research Institute of rural engineering, Water and Forests, University of CarthageFaculty of Sciences of Bizerte, University of CarthageNational research Institute of rural engineering, Water and Forests, University of CarthageRegional Field Crops Research Center of Beja, IRESANational research Institute of rural engineering, Water and Forests, University of CarthageAbstract Soil moisture has many important applications in subsurface water movement. Methods for measuring soil moisture are either destructive or require prior calibration. The aim of this study was to provide a framework for estimating soil water content with limited data (rainfall and evaporation). The hydraulic properties of four layers of soil from Tunisia were measured to a depth of 80 cm. These values were used as inputs to the Hydrus 1D model to generate soil water content profiles over a 27-year period. 109 profiles were calculated using machine learning algorithms (regression trees; random forest; support vector machine) to predict soil moisture content from daily values of rainfall and evaporation. The statistical performance indices of prediction, root mean square error and correlation coefficient indicate the superiority of regression trees over other methods for all soil layers and during both calibration and validation processes and reproducing the seasonal variation of soil moisture.https://doi.org/10.1007/s44378-025-00086-9Soil water contentHydrus-1DMachine learningModelling |
| spellingShingle | Sabri Kanzari Sana Ben Mariem Samir Ghannem Safouane Mouelhi Hiba Ghazouani Bechir Ben Nouna A framework based on mechanistic modelling and machine learning for soil moisture estimation Discover Soil Soil water content Hydrus-1D Machine learning Modelling |
| title | A framework based on mechanistic modelling and machine learning for soil moisture estimation |
| title_full | A framework based on mechanistic modelling and machine learning for soil moisture estimation |
| title_fullStr | A framework based on mechanistic modelling and machine learning for soil moisture estimation |
| title_full_unstemmed | A framework based on mechanistic modelling and machine learning for soil moisture estimation |
| title_short | A framework based on mechanistic modelling and machine learning for soil moisture estimation |
| title_sort | framework based on mechanistic modelling and machine learning for soil moisture estimation |
| topic | Soil water content Hydrus-1D Machine learning Modelling |
| url | https://doi.org/10.1007/s44378-025-00086-9 |
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