Soft and hard clustering methods for delineation of hydrological homogeneous regions in the southern strip of the Caspian Sea Watershed

Authors

  • S. Chavoshi,

    Corresponding author
    1. Department of Environmental Science, Faculty of Environmental Studies, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
    • Department of Hydrology, Soil Conservation and Watershed Research Institute of Iran, Tehran, Islamic Republic of Iran
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  • W.N. Azmin Sulaiman,

    1. Department of Environmental Science, Faculty of Environmental Studies, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
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  • B. Saghafian,

    1. Technical and Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran
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  • MD. N.B. Sulaiman,

    1. Department of Computer Science, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
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  • A.M. Latifah

    1. Department of Environmental Science, Faculty of Environmental Studies, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
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Correspondence:

Mr. Sattar Chavoshi, H2-19, CWMR, Mawson Lakes, UniSA, Mawson Lakes SA 5095, Australia

Email: chsatar@gmail.com

Abstract

Regionalisation is a fundamental task for transferring hydrological information from gauged to un-gauged catchments. This study employs several soft and hard clustering approaches for regionalisation of the 61 catchments located in the southern strip of the Caspian Sea Watershed. Factor analysis using Principal Component Analysis resulted to four out of 16 catchment's attributes relating to flooding which were then used for regionalisation. Hierarchical and Non-Hierarchical Clustering, K-Means, Fuzzy C-Means and Kohonen methods were studied and compared by L-Moment tests. While in hard clustering approach, distinct clusters of catchments were formed, soft clustering techniques allocated catchments to more than one cluster according to their membership probability. The performance of different clustering methods resulted to the similar number of homogeneous groups. However, the number of sites allocated to the clusters is different. Hard clustering resulted in three clusters at 38, 10 and 13 sites, while Soft clustering allocated 26, 20 and 15 sites to the clusters, respectively. Results indicate a superiority of the soft clustering in the study area where deriving hydrologic groups by hard clustering is problematic.

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