Regional frequency analysis using Growing Neural Gas network

JOURNAL OF HYDROLOGY

卷:550

Article

Abdi, Amin¹  Hassanzadeh, Yousef¹  Ouarda, Taha B. M. J.^2,3 

[1] Univ Tabriz, Dept Civil Engn, Tabriz, Iran

[2] Masdar Inst Sci & Technol, Inst Ctr Water & Environm, Abu Dhabi, U Arab Emirates

[3] INRS ETE, Natl Inst Sci Res, Quebec City, PQ, Canada

关键词: Regional frequency analysis;    Growing Neural Gas;    Minimum Description Length;    Clustering method;    L-moments;   

DOI  :  10.1016/j.jhydrol.2017.04.047

来源: Elsevier

【 摘 要 】

The delineation of hydrologically homogeneous regions is an important issue in regional hydrological frequency analysis. In the present study, an application of the Growing Neural Gas (GNG) network for hydrological data clustering is presented. The GNG is an incremental and unsupervised neural network, which is able to adapt its structure during the training procedure without using a prior knowledge of the size and shape of the network. In the GNG algorithm, the Minimum Description Length (MDL) measure as the cluster validity index is utilized for determining the optimal number of clusters (sub-regions). The capability of the proposed algorithm is illustrated by regionalizing drought severities for 40 synoptic weather stations in Iran. To fulfill this aim, first a clustering method is applied to form the sub-regions and then a heterogeneity measure is used to test the degree of heterogeneity of the delineated subregions. According to the MDL measure and considering two different indices namely CS and Davies-Bouldin (DB) in the GNG network, the entire study area is subdivided in two sub-regions located in the eastern and western sides of Iran. In order to evaluate the performance of the GNG algorithm, a number of other commonly used clustering methods, like K-means, fuzzy C-means, self-organizing map and Ward method are utilized in this study. The results of the heterogeneity measure based on the L-moments approach reveal that only the GNG algorithm successfully yields homogeneous sub-regions in comparison to the other methods. (C) 2017 Elsevier B.V. All rights reserved.

【 授权许可】

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