| Volumes and Issues Contents of Issue 6 |
www.hydrol-earth-syst-sci-discuss.net/6/7055/2009/
doi:10.5194/hessd-6-7055-2009
© Author(s) 2009. This work is distributed
under the Creative Commons Attribution 3.0 License.
Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology – Part 1: Concepts and methodology
1Centre for Advanced Numerical Simulation (CANSIM), Department of Civil & Geological Engineering, University of Saskatchewan, S7N 5A9 Saskatoon, SK, Canada
2Department of Hydroinformatics & Knowledge Management, UNESCO-IHE Institute for Water Education, Delft, The Netherlands
3Water Resources Section, Delft University of Technology, Delft, The Netherlands
Abstract. A comprehensive data driven modeling experiment is presented in 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 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 predictive accuracy and uncertainty of the modeling techniques can be evaluated. 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.
Discussion Paper (PDF, 750 KB) Interactive Discussion (Final Response, 3 Comments) Manuscript under review for HESS
Citation: Elshorbagy, A., Corzo, G., Srinivasulu, S., and Solomatine, D. P.: Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology – Part 1: Concepts and methodology, Hydrol. Earth Syst. Sci. Discuss., 6, 7055-7093, doi:10.5194/hessd-6-7055-2009, 2009. Bibtex EndNote Reference Manager XML
