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  1. An improved Kalman filtering approach for the estimation of unsaturated flow parameters by assimilating photographic imaging data
An improved Kalman filtering approach for the estimation of unsaturated flow parameters by assimilating photographic imaging data
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An improved Kalman filtering approach for the estimation of unsaturated flow parameters by assimilating photographic imaging data

Rajabi, M. M.

  1. DOI:10.1016/j.jhydrol.2020.125373
  2. Main Entry: Rajabi, M. M.
  3. Title:An improved Kalman filtering approach for the estimation of unsaturated flow parameters by assimilating photographic imaging data.
  4. Publisher:Elsevier B.V, 2020.
  5. Abstract:As a non-invasive method, photographic imaging techniques offer some interesting potentials for characterization of soil moisture content in unsaturated porous media, enabling mapping at very fine resolutions in both space and time. Although less explored, the wealth of soil moisture data provided by photographic imaging is also appealing for the estimation of unsaturated soil hydraulic parameters through inverse modeling. However, imaging data have some unique characteristics, including high susceptibility to noise, which can negatively affect the parameter estimation process. In this study a sequential data assimilation approach is developed to simultaneously update soil moisture content and soil hydraulic parameters using photographic imaging data. The study combines numerical modeling, polynomial chaos expansions, and a constrained restart dual ensemble Kalman filter (CRD-EnKF), to formulate a novel method that permits direct assimilation of imaging data. The proposed approach is validated using lab-scale data obtained from taking images of a two-dimensional plexiglass flow tank in a drainage/imbibition cycle. The study demonstrates the effectiveness of the proposed approach, and shows that in some cases, image-based hydraulic parameter estimations can outperform estimations obtained by using sparse direct, contact-based measurements of water content. We also propose a CRD-EnKF approach to estimate the conversion function that relates photographic light intensities to soil moisture content, without the use of direct measurements of the soil water content. The approach uses numerical model outputs instead of direct measurements, and can be useful when a good estimate of model input parameters is already available. © 2020 Elsevier B.V
  6. Notes:Sharif Repository
  7. Subject:Constrained restart dual ensemble Kalman filter.
  8. Subject:Parameter estimation.
  9. Subject:Photographic imaging.
  10. Subject:Soil moisture content.
  11. Subject:Unsaturated flow.
  12. Subject:Image enhancement.
  13. Subject:Imaging techniques.
  14. Subject:Inverse problems.
  15. Subject:Kalman filters.
  16. Subject:Moisture determination.
  17. Subject:Noninvasive medical procedures.
  18. Subject:Numerical methods.
  19. Subject:Numerical models.
  20. Subject:Porous materials.
  21. Subject:Soil moisture.
  22. Subject:Soil surveys.
  23. Subject:Water content.
  24. Subject:Conversion function.
  25. Subject:Ensemble Kalman Filter.
  26. Subject:Noninvasive methods.
  27. Subject:Parameter estimation process.
  28. Subject:Polynomial chaos expansion.
  29. Subject:Soil hydraulic parameters.
  30. Subject:Unsaturated porous media.
  31. Subject:Data assimilation.
  32. Subject:Data inversion.
  33. Subject:Image analysis.
  34. Subject:Imbibition.
  35. Subject:Kalman filter.
  36. Subject:Parameterization.
  37. Subject:Photography.
  38. Subject:Porous medium.
  39. Added Entry:Belfort, B.
  40. Added Entry:Lehmann, F.
  41. Added Entry:Weill, S.
  42. Added Entry:Ataie Ashtiani, B.
  43. Added Entry:Fahs, M.
  44. Added Entry:Sharif University of Technology.
  45. Source: Journal of Hydrology ; Volume 590 , 2020
  46. Web Site:https://www.sciencedirect.com/science/article/abs/pii/S0022169420308337

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