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Identification of homogeneous regions for regionalization of watersheds by two-level self-organizing feature maps

Farsadnia, F., Rostami Kamrood, M., Moghaddam Nia, A., Modarres, R., Bray, Michaela, Han, D. and Sadatinejad, J. 2014. Identification of homogeneous regions for regionalization of watersheds by two-level self-organizing feature maps. Journal of Hydrology 509 , pp. 387-397. 10.1016/j.jhydrol.2013.11.050

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Abstract

One of the several methods in estimating flood quantiles in ungauged or data-scarce watersheds is regional frequency analysis. Amongst the approaches to regional frequency analysis, different clustering techniques have been proposed to determine hydrologically homogeneous regions in the literature. Recently, Self-Organization feature Map (SOM), a modern hydroinformatic tool, has been applied in several studies for clustering watersheds. However, further studies are still needed with SOM on the interpretation of SOM output map for identifying hydrologically homogeneous regions. In this study, two-level SOM and three clustering methods (fuzzy c-mean, K-mean, and Ward’s Agglomerative hierarchical clustering) are applied in an effort to identify hydrologically homogeneous regions in Mazandaran province watersheds in the north of Iran, and their results are compared with each other. Firstly the SOM is used to form a two-dimensional feature map. Next, the output nodes of the SOM are clustered by using unified distance matrix algorithm and three clustering methods to form regions for flood frequency analysis. The heterogeneity test indicates the four regions achieved by the two-level SOM and Ward approach after adjustments are sufficiently homogeneous. The results suggest that the combination of SOM and Ward is much better than the combination of either SOM and FCM or SOM and K-mean.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: Q Science > QC Physics
T Technology > TC Hydraulic engineering. Ocean engineering
Uncontrolled Keywords: Regionalization; Self-organization feature maps; Clustering methods; Hydrologic homogeneity; Cluster validation measures
Publisher: Elsevier
ISSN: 0022-1694
Last Modified: 21 Feb 2019 16:21
URI: http://orca.cf.ac.uk/id/eprint/72960

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