Balemans, Sophie

Nopens, Ingmar

Seuntjens, Piet

Van Hoey, Stijn

English

Ghent University Academic Bibliography

Geophysical Research AbstractsVol. 17, EGU2015-9033, 2015EGU General Assembly 2015© Author(s) 2015. CC Attribution 3.0 License.Interactive model evaluation tool based on IPython notebookSophie Balemans (1), Stijn Van Hoey (1), Ingmar Nopens (1), Piet Seuntjes (2,3,4)(1) Ghent University, Faculty of Bioscience Engineering, Department of Mathematical Modelling, Statistics andBioinformatics, BIOMATH, Ghent, Belgium (sophie.balemans@ugent.be), (2) Ghent University, Faculty of BioscienceEngineering, Department of Soil Management, Gent, Belgium (piet.seuntjens@vito.be), (3) Flemish Institute forTechnological Research (VITO), Environmental modelling Unit, Mol, Belgium , (4) University of Antwerp, Department ofBioscience Engineering, Antwerp, BelgiumIn hydrological modelling, some kind of parameter optimization is mostly performed. This can be the selection ofa single best parameter set, a split in behavioural and non-behavioural parameter sets based on a selected thresholdor a posterior parameter distribution derived with a formal Bayesian approach. The selection of the criterion tomeasure the goodness of fit (likelihood or any objective function) is an essential step in all of these methodologiesand will affect the final selected parameter subset. Moreover, the discriminative power of the objective function isalso dependent from the time period used.In practice, the optimization process is an iterative procedure. As such, in the course of the modelling process, anincreasing amount of simulations is performed. However, the information carried by these simulation outputs isnot always fully exploited. In this respect, we developed and present an interactive environment that enables theuser to intuitively evaluate the model performance. The aim is to explore the parameter space graphically and tovisualize the impact of the selected objective function on model behaviour.First, a set of model simulation results is loaded along with the corresponding parameter sets and a data set ofthe same variable as the model outcome (mostly discharge). The ranges of the loaded parameter sets define theparameter space. A selection of the two parameters visualised can be made by the user. Furthermore, an objectivefunction and a time period of interest need to be selected. Based on this information, a two-dimensional parameterresponse surface is created, which actually just shows a scatter plot of the parameter combinations and assignsa color scale corresponding with the goodness of fit of each parameter combination. Finally, a slider is availableto change the color mapping of the points. Actually, the slider provides a threshold to exclude non behaviourparameter sets and the color scale is only attributed to the remaining parameter sets. As such, by interactivelychanging the settings and interpreting the graph, the user gains insight in the model structural behaviour. Moreover,a more deliberate choice of objective function and periods of high information content can be identified.The environment is written in an IPython notebook and uses the available interactive functions provided by theIPython community. As such, the power of the IPython notebook as a development environment for scientificcomputing is illustrated (Shen, 2014).

Interactive model evaluation tool based on IPython notebook

https://biblio.ugent.be/publication/6829847/file/6829863.pdf

Interactive model evaluation tool based on IPython notebook

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Ghent University Academic Bibliography