- author = "A. Elshorbagy and G. Corzo and S. Srinivasulu and D. P. Solomatine",
- title = "Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology",
- journal = "Hydrology and Earth System Sciences",
- year = "2010",
- volume = "14",
- number = "10",
- pages = "1931--1941",
- month = "14 " # oct,
- keywords = "genetic algorithms, genetic programming",
- ISSN = "1471-2164",
-
URL = "
http://www.hydrol-earth-syst-sci.net/14/1931/2010/hess-14-1931-2010.pdf",
-
URL = "
http://www.hydrol-earth-syst-sci.net/14/1931/2010/",
-
DOI = "
doi:10.5194/hess-14-1931-2010",
- publisher = "Copernicus GmbH",
- abstract = "A comprehensive data driven modelling 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 modelling (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 neighbours are proposed and explained. Multiple linear regression and na{\"i}ve 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 modelling experiment. Twelve different realisations (groups) from each dataset are created by a procedure involving random sampling. Each group contains three subsets; training, cross-validation, and testing. Each modelling technique is proposed to be applied to each of the 12 groups of each dataset. This way, both prediction accuracy and uncertainty of the modelling techniques can be evaluated. The description of the data sets, the implementation of the modeling techniques, results and analysis, and the findings of the modelling experiment are deferred to the second part of this paper.",
-
notes = "See also \cite{Elshorbagy:2010a:HESS} Published in
Hydrol. Earth Syst. Sci. Discuss.: 19 November 2009
\cite{oai:doaj-articles:09c2f3076a15547532440e3ac274c044}
and
\cite{oai:doaj-articles:90e50b27744c40b3f9d0243d0896b665}
http://www.hydrol-earth-syst-sci-discuss.net/6/7055/2009/hessd-6-7055-2009.pdf
cite{hessd-6-7055-2009}.
1 Centre for Advanced Numerical Simulation (CANSIM), Department of Civil and Geological Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada 2 Department of Hydroinformatics and Knowledge Management, UNESCO-IHE Institute for Water Education, Delft, The Netherlands 3 Water Resources Section, Delft University of Technology, Delft, The Netherlands",
