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| Titel |
Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling |
| VerfasserIn |
S. Galelli, A. Castelletti |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 17, no. 7 ; Nr. 17, no. 7 (2013-07-11), S.2669-2684 |
| Datensatznummer |
250018930
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| Publikation (Nr.) |
 copernicus.org/hess-17-2669-2013.pdf |
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| Zusammenfassung |
| Combining randomization methods with ensemble prediction is emerging
as an effective option to balance accuracy and computational
efficiency in data-driven modelling. In this paper, we investigate the
prediction capability of extremely randomized trees (Extra-Trees), in
terms of accuracy, explanation ability and computational efficiency,
in a streamflow modelling exercise. Extra-Trees are a totally
randomized tree-based ensemble method that (i) alleviates the poor
generalisation property and tendency to overfitting of traditional
standalone decision trees (e.g. CART); (ii) is computationally
efficient; and, (iii) allows to infer the relative importance of the input variables, which
might help in the ex-post physical interpretation of the model. The
Extra-Trees potential is analysed on two real-world case studies – Marina
catchment (Singapore) and Canning River (Western Australia) – representing
two different morphoclimatic contexts. The evaluation is performed against
other tree-based methods (CART and M5) and parametric data-driven
approaches (ANNs and multiple linear regression). Results show that
Extra-Trees perform comparatively well to the best of the benchmarks
(i.e. M5) in both the watersheds, while outperforming the other
approaches in terms of computational requirement when adopted on large
datasets. In addition, the ranking of the input variable provided can
be given a physically meaningful interpretation. |
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