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@ARTICLE{Kotlar:866810,
      author       = {Kotlar, Ali Mehmandoost and de Jong van Lier, Quirijn and
                      Barros, Alexandre Hugo C. and Iversen, Bo V. and Vereecken,
                      Harry},
      title        = {{D}evelopment and {U}ncertainty {A}ssessment of
                      {P}edotransfer {F}unctions for {P}redicting {W}ater
                      {C}ontents at {S}pecific {P}ressure {H}eads},
      journal      = {Vadose zone journal},
      volume       = {18},
      number       = {1},
      issn         = {1539-1663},
      address      = {Alexandria, Va.},
      publisher    = {GeoScienceWorld},
      reportid     = {FZJ-2019-05873},
      pages        = {190063 -},
      year         = {2019},
      abstract     = {There has been much effort to improve the performance of
                      pedotransfer functions (PTFs) using intelligent algorithms,
                      but the issue of covariate shift, i.e., different
                      probability distributions in training and testing datasets,
                      and its impact on prediction uncertainty of PTFs has been
                      rarely addressed. The common practice in PTF generation is
                      to randomly separate the dataset into training and testing
                      subsets, and the outcomes of this random selection may be
                      different if the process is subject to covariate shift. We
                      evaluated the impact of covariate shift generated by data
                      shuffling and detected by Kolmogorov–Smirnov test for the
                      prediction of water contents using soil databases from
                      Denmark and Brazil. The soil water contents at different
                      pressure heads were predicted by developing linear and
                      stepwise regression besides machine learning based PTFs
                      including Gaussian process regression and ensemble method.
                      Regression based PTFs for the Brazilian dataset resulted in
                      better predictions compared with machine learning methods,
                      which in their turn estimated high water contents in Danish
                      soils more accurately. One hundred PTFs were developed for
                      water content at specific pressure heads by data shuffling.
                      From these, 100 sets of fitted van Genuchten parameters were
                      obtained representing the generated uncertainty. Data
                      shuffling led to covariate shift, resulting in uncertainty
                      in water content prediction by the PTFs. Inherent
                      variability of data may lead to increased prediction
                      uncertainty. For correlated data, simple regression models
                      performed as good as sophisticated machine learning methods.
                      Using PTF‐predicted water contents for van Genuchten
                      retention parameter fitting may lead to a high uncertainty.},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000495432100001},
      doi          = {10.2136/vzj2019.06.0063},
      url          = {https://juser.fz-juelich.de/record/866810},
}