Download Regionalization of Watersheds An Approach Based on Cluster by A.R. Rao, V. V. Srinivas PDF

By A.R. Rao, V. V. Srinivas

Clustering recommendations are used to spot teams of watersheds that have comparable flood features. This e-book, the 1st of its style, is a accomplished reference on how one can use those concepts for nearby flood frequency research. It offers an in depth account of numerous lately built clustering suggestions, together with these in accordance with fuzzy set idea. It additionally brings jointly previously scattered study findings at the software of clustering strategies to RFFA.

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Extra resources for Regionalization of Watersheds An Approach Based on Cluster Analysis

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The sites excluded from a region are examined to see whether they fit in any other region. In some instances, a site excluded from one region would fit in more than one region. Such a site is considered to be common to all the concerned regions. Among the ten clusters identified as optimal partition for the Indiana data, the second cluster had just five sites. Following option (v) for adjusting the regions, this cluster is broken-up by transferring the sites contained in it to other regions. Region-1 is obtained by merging clusters 6 and 8.

This method is known for its efficiency in clustering large data sets with numerical attributes. However, it has limitations in clustering categorical data (Ralambondrainy, 1995; Huang and Ng, 2003). Further, the method is sensitive to the presence of outliers. In K-medoids method, median of each cluster is considered as its representative. This has two advantages. First, the method can be used with both numerical and categorical attributes, and, second, the choice of medoids is dictated by the location of a predominant fraction of data points inside a cluster and, therefore, it is less sensitive to the presence of outliers (Berkhin, 2002).

The description length of the outlier set O, denoted by mod L(O), is usually encoded in the same way as the prototype vectors. The capability of the model to describe the whole data set X = I + O is reflected by the last two terms in Eq. 25). Let b denote the number of bits needed for encoding a single data vector. , number of feature vectors) of the outlier set. The b is computed using the average value range of rescaled feature vectors and the resolution (or accuracy) of data η as b = [log2 (range/η)].

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