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首页> 外文期刊>Journal of Hydroinformatics >Assessment of some combinations of hard and fuzzy clustering techniques for regionalisation of catchments in Sefidroud basin
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Assessment of some combinations of hard and fuzzy clustering techniques for regionalisation of catchments in Sefidroud basin

机译：Sefidroud盆地集水区划的硬聚类和模糊聚类技术的组合评估

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摘要

Cluster analysis methods are a type of well-known technique for regionalisation of catchments to perform regional flood frequency analysis. In this study, a fuzzy extension of hybrid clustering algorithms is evaluated. Self-organizing feature maps and four hierarchical clustering algorithms were used to provide the initial cluster centres for fuzzy c-means (FCM) algorithm. The hybrid approach was used for regionalisation of catchments in Sefidroud basin based on feature vectors including five catchment attributes: longitude and latitude, drainage area, runoff coefficient and mean annual precipitation. The results showed that according to the values of both the objective function and the cluster validity indices, the performances of FCM algorithm often was improved by using the proposed hybrid approach. Also, it was evident from the results that in the case of minimizing the objective function, the combination of Ward's algorithm and FCM provided best results, but according to the cluster validity indices, other hybrid algorithms such as combinations of single linkage or complete linkage and FCM algorithm presented the most desirable results. In addition, according to the results, there are two well-defined homogeneous regions in Sefidroud basin identified by all the examined hybrid algorithms.
机译：聚类分析方法是一种用于流域区域化以执行区域洪水频率分析的著名技术。在这项研究中，对混合聚类算法的模糊扩展进行了评估。自组织特征图和四种分层聚类算法用于提供模糊c均值（FCM）算法的初始聚类中心。基于特征向量，包括五种集水属性：经度和纬度，流域，径流系数和年平均降水量，将混合方法用于塞菲德罗德盆地的集水区划。结果表明，根据目标函数和聚类有效性指标两者的值，使用所提出的混合方法常常可以提高FCM算法的性能。同样，从结果中可以明显看出，在最小化目标函数的情况下，沃德算法和FCM的组合提供了最佳结果，但是根据聚类有效性指标，还可以使用其他混合算法，例如单连接或完全连接的组合以及FCM算法提出了最理想的结果。此外，根据结果，所有检查过的混合算法都在Sefidroud盆地中确定了两个明确定义的均质区域。

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