@article{WaldripNivenAbeletal.2017,
  author    = {Waldrip, S. H. and Niven, Robert K. and Abel, Markus and Schlegel, M.},
  title     = {Reduced-Parameter Method for Maximum Entropy Analysis of Hydraulic Pipe Flow Networks},
  series = {Journal of hydraulic engineering},
  volume    = {144},
  journal   = {Journal of hydraulic engineering},
  number    = {2},
  publisher = {American Society of Civil Engineers},
  address   = {Reston},
  issn      = {0733-9429},
  doi       = {10.1061/(ASCE)HY.1943-7900.0001379},
  pages     = {10},
  year      = {2017},
  abstract  = {A maximum entropy (MaxEnt) method is developed to predict flow rates or pressure gradients in hydraulic pipe networks without sufficient information to give a closed-form (deterministic) solution. This methodology substantially extends existing deterministic flow network analysis methods. It builds on the MaxEnt framework previously developed by the authors. This study uses a continuous relative entropy defined on a reduced parameter set, here based on the external flow rates. This formulation ensures consistency between different representations of the same network. The relative entropy is maximized subject to observable constraints on the mean values of a subset of flow rates or potential differences, the frictional properties of each pipe, and physical constraints arising from Kirchhoff's first and second laws. The new method is demonstrated by application to a simple one-loop network and a 1,123-node, 1,140-pipe water distribution network in the suburb of Torrens, Australian Capital Territory, Australia.},
  language  = {en}
}
@article{WaldripNivenAbeletal.2016,
  author    = {Waldrip, S. H. and Niven, R. K. and Abel, Markus and Schlegel, M.},
  title     = {Maximum Entropy Analysis of Hydraulic Pipe Flow Networks},
  series = {Journal of hydraulic engineering},
  volume    = {142},
  journal   = {Journal of hydraulic engineering},
  publisher = {American Society of Civil Engineers},
  address   = {Reston},
  issn      = {0733-9429},
  doi       = {10.1061/(ASCE)HY.1943-7900.0001126},
  pages     = {332 -- 347},
  year      = {2016},
  language  = {en}
}
@article{VorpahlElsenbeerMaerkeretal.2012,
  author    = {Vorpahl, Peter and Elsenbeer, Helmut and M{\"a}rker, Michael and Schr{\"o}der-Esselbach, Boris},
  title     = {How can statistical models help to determine driving factors of landslides?},
  series = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog},
  volume    = {239},
  journal   = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog},
  number    = {7},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0304-3800},
  doi       = {10.1016/j.ecolmodel.2011.12.007},
  pages     = {27 -- 39},
  year      = {2012},
  abstract  = {Landslides are a hazard for humans and artificial structures. From an ecological point of view, they represent an important ecosystem disturbance, especially in tropical montane forests. Here, shallow translational landslides are a frequent natural phenomenon and one local determinant of high levels of biodiversity. In this paper, we apply weighted ensembles of advanced phenomenological models from statistics and machine learning to analyze the driving factors of natural landslides in a tropical montane forest in South Ecuador. We exclusively interpret terrain attributes, derived from a digital elevation model, as proxies to several driving factors of landslides and use them as predictors in our models which are trained on a set of five historical landslide inventories. We check the model generality by transferring them in time and use three common performance criteria (i.e. AUC, explained deviance and slope of model calibration curve) to, on the one hand, compare several state-of-the-art model approaches and on the other hand, to create weighted model ensembles. Our results suggest that it is important to consider more than one single performance criterion. Approaching our main question, we compare responses of weighted model ensembles that were trained on distinct functional units of landslides (i.e. initiation, transport and deposition zones). This way, we are able to show that it is quite possible to deduce driving factors of landslides, if the consistency between the training data and the processes is maintained. Opening the 'black box' of statistical models by interpreting univariate model response curves and relative importance of single predictors regarding their plausibility, we provide a means to verify this consistency. With the exception of classification tree analysis, all techniques performed comparably well in our case study while being outperformed by weighted model ensembles. Univariate response curves of models trained on distinct functional units of landslides exposed different shapes following our expectations. Our results indicate the occurrence of landslides to be mainly controlled by factors related to the general position along a slope (i.e. ridge, open slope or valley) while landslide initiation seems to be favored by small scale convexities on otherwise plain open slopes.},
  language  = {en}
}