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| Titel |
Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology |
| 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.1931-1941 |
| Datensatznummer |
250012445
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| Publikation (Nr.) |
 copernicus.org/hess-14-1931-2010.pdf |
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| Zusammenfassung |
| A comprehensive data driven modeling experiment is presented in a two-part
paper. In this first part, an extensive data-driven modeling experiment is
proposed. The most important concerns regarding the way data driven modeling
(DDM) techniques and data were handled, compared, and evaluated, and the
basis on which findings and conclusions were drawn are discussed. A concise
review of key articles that presented comparisons among various DDM
techniques is presented. Six DDM techniques, namely, neural networks,
genetic programming, evolutionary polynomial regression, support vector
machines, M5 model trees, and K-nearest neighbors are proposed and
explained. Multiple linear regression and naïve models are also
suggested as baseline for comparison with the various techniques. Five
datasets from Canada and Europe representing evapotranspiration, upper and
lower layer soil moisture content, and rainfall-runoff process are described
and proposed, in the second paper, for the modeling experiment. Twelve
different realizations (groups) from each dataset are created by a procedure
involving random sampling. Each group contains three subsets; training,
cross-validation, and testing. Each modeling technique is proposed to be
applied to each of the 12 groups of each dataset. This way, both prediction
accuracy and uncertainty of the modeling techniques can be evaluated. The
description of the datasets, the implementation of the modeling techniques,
results and analysis, and the findings of the modeling experiment are
deferred to the second part of this paper. |
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