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| Titel |
Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application |
| VerfasserIn |
A. Elshorbagy, G. Corzo, S. Srinivasulu, D. P. Solomatine |
| 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 ; 14, no. 10 ; Nr. 14, no. 10 (2010-10-14), S.1943-1961 |
| Datensatznummer |
250012446
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| Publikation (Nr.) |
 copernicus.org/hess-14-1943-2010.pdf |
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| Zusammenfassung |
| In this second part of the two-part paper, the data driven modeling (DDM)
experiment, presented and explained in the first part, is implemented.
Inputs for the five case studies (half-hourly actual evapotranspiration,
daily peat soil moisture, daily till soil moisture, and two daily
rainfall-runoff datasets) are identified, either based on previous studies
or using the mutual information content. Twelve groups (realizations) were
randomly generated from each dataset by randomly sampling without
replacement from the original dataset. Neural networks (ANNs), genetic
programming (GP), evolutionary polynomial regression (EPR), Support vector
machines (SVM), M5 model trees (M5), K-nearest neighbors (K-nn), and
multiple linear regression (MLR) techniques are implemented and applied to
each of the 12 realizations of each case study. The predictive accuracy and
uncertainties of the various techniques are assessed using multiple average
overall error measures, scatter plots, frequency distribution of model
residuals, and the deterioration rate of prediction performance during the
testing phase. Gamma test is used as a guide to assist in selecting the
appropriate modeling technique. Unlike two nonlinear soil moisture case
studies, the results of the experiment conducted in this research study show
that ANNs were a sub-optimal choice for the actual evapotranspiration and
the two rainfall-runoff case studies. GP is the most successful technique
due to its ability to adapt the model complexity to the modeled data. EPR
performance could be close to GP with datasets that are more linear than
nonlinear. SVM is sensitive to the kernel choice and if appropriately
selected, the performance of SVM can improve. M5 performs very well with
linear and semi linear data, which cover wide range of hydrological
situations. In highly nonlinear case studies, ANNs, K-nn, and GP could be
more successful than other modeling techniques. K-nn is also successful in
linear situations, and it should not be ignored as a potential modeling
technique for hydrological applications. |
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