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| Titel |
The application of data mining techniques for the regionalisation of hydrological variables |
| VerfasserIn |
M. J. Hall, A. W. Minns, A. K. M. Ashrafuzzaman |
| 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 ; 6, no. 4 ; Nr. 6, no. 4, S.685-694 |
| Datensatznummer |
250003666
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| Publikation (Nr.) |
 copernicus.org/hess-6-685-2002.pdf |
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| Zusammenfassung |
| Flood quantile estimation for ungauged
catchment areas continues to be a routine problem faced by the practising
Engineering Hydrologist, yet the hydrometric networks in many countries are
reducing rather than expanding. The result is an increasing reliance on methods
for regionalising hydrological variables. Among the most widely applied
techniques is the Method of Residuals, an iterative method of classifying
catchment areas by their geographical proximity based upon the application of
Multiple Linear Regression Analysis (MLRA). Alternative classification
techniques, such as cluster analysis, have also been applied but not on a
routine basis. However, hydrological regionalisation can also be regarded as a
problem in data mining — a search for useful knowledge and models embedded
within large data sets. In particular, Artificial Neural Networks (ANNs) can be
applied both to classify catchments according to their geomorphological and
climatic characteristics and to relate flow quantiles to those characteristics.
This approach has been applied to three data sets from the south-west of England
and Wales; to England, Wales and Scotland (EWS); and to the islands of Java and
Sumatra in Indonesia. The results demonstrated that hydrologically plausible
clusters can be obtained under contrasting conditions of climate. The four
classes of catchment found in the EWS data set were found to be compatible with
the three classes identified in the earlier study of a smaller data set from
south-west England and Wales. Relationships for the parameters of the at-site
distribution of annual floods can be developed that are superior to those based
upon MLRA in terms of root mean square errors of validation data sets. Indeed,
the results from Java and Sumatra demonstrate a clear advantage in reduced root
mean square error of the dependent flow variable through recognising the
presence of three classes of catchment. Wider evaluation of this methodology is
recommended.
Keywords: regionalisation, floods, catchment
characteristics, data mining, artificial neural networks |
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