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Journal of Engineering and Applied Sciences
Year: 2007  |  Volume: 2  |  Issue: 3  |  Page No.: 524 - 530

A Comparison of Low-Flow Clustering Methods: Streamflow Grouping

Ercan Kahya and M. Cuneyd Demirel    

Abstract: In this study, three clustering algorithms which use agglomerative clustering procedure to identify groups of similar catchments are investigated to determine their effectiveness in low flow clustering scheme. These hierarchical clustering algorithms are single linkage, complete linkage and Ward`s algorithms. The effectiveness of the cluster analysis algorithms is investigated by using monthly minimum streamflow data recorded from watersheds in Turkey with the period of 1964-1994. Furthermore one of cluster validity indices (namely, cophenet correlation coefficient index) is used to strengthen our results. The hierarchical cluster analysis is found to be useful in minimizing efforts needed to identify homogeneous clusters. Ward`s algorithm with Euclidean metric is the one to decrease the variance in each group and appears to be favourable in the applications of low-flow. The results and comparison with our previous studies are also presented in the thematic maps.

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