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Water quality comprehensive evaluation method for large water distribution network based on clustering analysis

机译:基于聚类分析的大型供水管网水质综合评价方法

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摘要

In order to evaluate water quality for a large water distribution network comprehensively, a two-stage classification method was used and the clustering methods, self-organizing map (SOM), K-means method and fuzzy c-mean (FCM), were represented. With these clustering methods, the pipes of a large real water distribution network were divided into some groups considering one or more water quality indicators synchronously. The water quality indicators of residual chlorine, water age, THMs, TAAs, TOC and BDOC are used in this paper. Residual chlorine and water age are two main water quality indicators. THMs and TAAs can represents the disinfection byproducts information. And TOC and BDOC are used to represents biological stability. According to the clustering results, the status of water quality of the water network was analysed. The results showed that the classification of SOM could express the comprehensive water quality in a water distribution network (WDN) directly and vividly by high-dimension water quality indicator projection to a low dimensional topology grid and that two-stage classification method has higher efficiency in comparison to the traditional clustering method. Water quality comprehensive evaluation was of significance for locating water quality monitoring, water network rehabilitation and expansion.
机译:为了全面评价大型供水网络的水质,采用了两阶段分类法,并提出了聚类方法,自组织图(SOM),K-均值法和模糊c-均值(FCM)。 。通过这些聚类方法,将大型真实配水管网的管道分为几类,同时考虑一个或多个水质指标。本文使用残留氯,水龄,THMs,TAA,TOC和BDOC的水质指标。余氯和水龄是两个主要的水质指标。 THM和TAA可以代表消毒副产品信息。 TOC和BDOC用于表示生物稳定性。根据聚类结果,分析了水网的水质状况。结果表明,通过将高维水质指标投影到低维拓扑网格上,SOM的分类可以直接,生动地表达供水网络中的综合水质,并且两阶段分类方法在水网中的分类效率更高。与传统聚类方法的比较。水质综合评价对水质监测,水网修复和扩建具有重要意义。

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