Application of Clustering Techniques Using Prioritized Variables in Regional Flood Frequency Analysis—Case Study of Mahanadi Basin
Publication: Journal of Hydrologic Engineering
Volume 17, Issue 1
Abstract
The selection of suitable site characteristics and the number of clusters play an important role for finding homogeneous regions in regional flood frequency analysis. The present study investigates the partition of the Mahanadi basin into homogeneous regions by applying different clustering techniques by using fewer but influential variables. As such, the entire basin is not hydrometeorologically homogeneous. Principal component analysis has been initiated in finding appropriate site characteristics (variables) as per priority. Out of seven variables, four variables are selected on priority. Possible numbers of cluster are found by applying the Kohonen self-organization map and Andrews plot. Other clustering techniques, such as hierarchical clustering fuzzy C-mean (FCM) and -mean, are applied on prioritized variables to verify the result of clustering. The intercomparison of clustering techniques gives the optimum number of sites to be placed in a particular cluster. The sites clustered as per FCM give more homogenous results. The entire basin is divided into two homogeneous clusters. The regional L-moment algorithm is used to test the homogeneity and to identify an appropriate underlying frequency distribution. An index flood is also stated relating to catchment characteristics by using the multiple linear regression approach. The results are compared with the previous studies of flood frequency on this basin. The study faces the limitation of less data availability to predict longer return period values ( ).
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Published In
Journal of Hydrologic Engineering
Volume 17 • Issue 1 • January 2012
Pages: 213 - 223
Copyright
© 2012 American Society of Civil Engineers.
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Received: Aug 10, 2010
Accepted: Apr 15, 2011
Published online: Apr 18, 2011
Published in print: Jan 1, 2012
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