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Application of Surface Water Quality Classification Models Using Principal Components Analysis and Cluster Analysis

机译:主成分分析和聚类分析在地表水水质分类模型中的应用

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Water quality monitoring has one of the highest priorities in surface water protection policy. Many techniques and methods focus in analyzing the concealing parameters that determine the variance of observed water quality of various source points. A considerable proportion of them mainly depend on statistical methods, multivariate statistical techniques in particular.In the present study, the use of multivariate techniques is required to reduce the large variables number of Nile River water quality upstream Cairo Drinking Water Plants (CDWPs) and determination of relationships among them for easy and robust evaluation. By means of multivariate statistics of principal components analysis (PCA), Fuzzy C-Means (FCM) and K-means algorithm for clustering analysis, this study attempted to determine the major dominant factors responsible for the variations of Nile River water quality upstream Cairo Drinking Water Plants (CDWPs).Furthermore, cluster analysis classified 21 sampling stations into three clusters based on similarities of water quality features.The result of PCA shows that 6 principal components contain the key variables and account for 75.82% of total variance of the study area surface water quality and the dominant water quality parameters were: Conductivity, Iron, Biological Oxygen Demand (BOD), Total Coliform (TC), Ammonia (NH3), and pH.However, the results from both of FCM clustering and K-means algorithm, based on the dominant parameters concentrations, determined 3 cluster groups and produced cluster centers (prototypes). Based on clustering classification, a noted water quality deteriorating as the cluster number increased from one to three, thus the cluster grouping can be used to identify the physical, chemical and biological processes creating the variations in the water quality parameters.
机译:在地表水保护政策中,水质监测是最高优先事项之一。许多技术和方法专注于分析确定各个源点观测水质方差的隐藏参数。其中很大一部分主要依靠统计方法,尤其是多元统计技术。在本研究中,需要使用多元技术来减少尼罗河上游开罗饮用水厂(CDWPs)的尼罗河水质的大数目和确定它们之间的关系,以便轻松,强大地进行评估。通过主成分分析(PCA),模糊C均值(FCM)和K均值算法的多元统计分析进行聚类分析,本研究试图确定导致开罗河上游尼罗河水质变化的主要主导因素此外,根据水质特征的相似性,聚类分析将21个采样站分为三个聚类,PCA结果显示6个主要成分包含关键变量,占研究区域总变异的75.82%地表水水质和主要水质参数为:电导率,铁,生物需氧量(BOD),总大肠菌群(TC),氨(NH3)和pH,但是FCM聚类和K-均值算法的结果,根据主要参数浓度,确定了3个簇群并产生了簇中心(原型)。基于聚类分类,随着聚类数从一增加到三,注意到的水质会恶化,因此聚类分组可用于识别物理,化学和生物过程,从而造成水质参数的变化。

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