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Computer Supported Knowledge Discovery -- a Case Study in Flow Resistance Induced by Vegetation

Author(s): Vladan Babovic; Maarten Keijzer

Linked Author(s): Vladan Babovic


Abstract: Data Mining and Knowledge Discovery aims at providing tools to facilitate the conversion of data to a better understanding of processes that generated or produced those data. We call this the mining of data for knowledge. Data mining extracts patterns from data. It creates models from data, by using for example, genetic programming, polynomial or artificial neural networks, or even support vector machines. These new models, combined with the understanding of the physical processes -- the theory -- can result in an improved understanding and novel formulations of physical laws and an improved predictive capability. The present paper describes some of the very first efforts under the D2K (Data to Knowledge) Research Project currently conducted at Danish Hydraulic Institute with a support from the Danish Technical Research Council (STVF). The paper firstly outlines elementary data mining principles, particularly when applied to analysis of scientific data. In the second half of the contribution, results obtained through analysis of the data related to the additional resistance to the flow induced by flexible vegetation are presented. The data are analysed by the means of genetic programming (GP). Induced formulations and discussed in terms of accuracy and physical interpretability.


Year: 1999

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