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| Titel |
Optimising soil-hydrological predictions using effective CART models |
| VerfasserIn |
B. Selle, B. Huwe |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7340
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| Digitales Dokument |
URL |
| Erschienen |
In: Proceedings of the 8th Workshop for Large Scale Hydrological Modelling - Oppurg 2004 ; Nr. 5 (2005-12-16), S.37-41 |
| Datensatznummer |
250002165
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| Publikation (Nr.) |
 copernicus.org/adgeo-5-37-2005.pdf |
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| Zusammenfassung |
| There are various problems with process-based models at the landscape scale,
including substantial computational requirements, a multitude of uncertain
input parameters and the limited parameter identificability. Classification
And Regression Trees (CART) is a recent data-based approach that is likely
to yield advantages both over process-based models and simple empirical
models. This non-parametric regression technique can be used to simplify
process-based models by extracting key variables, which govern the process
of interest at a specified scale. In other words, the model complexity can
be fitted to the information content in the data. CART is applied to model
spatially distributed percolation in soils using weather data and the
groundwater depths specific to the site. The training data was obtained by
numerical experiments with Hydrus1D. Percolation is effectively predicted
using CART but the model performance is highly dependant on the available
data and the boundary conditions. However, the effective CART models possess
an optimal complexity that corresponds to the information content in the
data and hence, are particularly suited for environmental management
purposes. |
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