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An integrated approach for assessing surface water quality: Case of Beni Haroun dam (Northeast Algeria)

机译：评估地表水质的综合方法：Beni Haroun Dam（东北阿尔及利亚）

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摘要

In this paper, we use an integrated approach to carry out a comprehensive evaluation of water quality in the Beni Haroun (BH) dam, the largest surface water resource in Algeria. Several techniques have been employed under the same framework, including the Canadian Council Ministers Environment Water Quality Index (CCME-WQI), principal component analysis and factor analysis (PCA/FA), the K-means clustering, and the ordinary least square (OLS) analysis. A data set of 22 physicochemical parameters has been collected, over a period of 11 years, from three sampling stations: Ain Smara (ST1) and Menia (ST2), both located upstream of "Wadi Rhumel," and BH dam station (ST3), located at the dam site. The PCA/FA enables the identification of seven key factors that influence significantly BH dam water quality. The average values of CCME indices at the BH dam were 17, 40, 42, and 32 for drinking, irrigation, industry, and aquatic life purposes, respectively, which indicate poor water quality, according to the CCME categorization scheme. Besides, the K-means algorithm has been proven to be a very useful machine learning tool to detect that the major source of BH dam pollution is "Wadi Rhumel." Finally, OLS analysis, along with the Mann-Kendall test, highlighted the positive trend of BH dam's water quality.
机译：在本文中，我们利用综合方法在阿尔及利亚最大的地表水资源中进行了对比金柱（BH）大坝水质的综合评价。在同一框架下采用了几种技术，包括加拿大委员会部长环境水质指数（CCME-WQI），主成分分析和因子分析（PCA / FA），K-Means Clustering和普通最小二乘（OLS ） 分析。从三个采样站（Ain Smara（ST1）和Menia（ST2），位于“Wadi Rhumel”和BH Dam Station的上游（ST3），已经收集了22个物理化学参数的数据集。（ST3） ，位于大坝站点。 PCA / FA能够识别七个关键因素，影响大坝水质的明显。根据CCME分类方案，BH大坝的CCME指数的平均值分别为17,40,42和32次饮用，灌溉，工业和水生寿命，表明水质不佳。此外，K-Means算法已被证明是一种非常有用的机器学习工具，以检测到BH坝污染的主要来源是“Wadi Rhumel”。最后，OLS分析以及Mann-Kendall测试，突出了BH大坝水质的积极趋势。

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