Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquia River basin (Cordoba-Argentina)
Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquia River basin (Cordoba-Argentina)
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DOI：
10.1016/s0043-1354(00)00592-3
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发表时间：
2001-08-01
期刊：
影响因子：
12.8
通讯作者：
De Los Angeles, BM
中科院分区：
文献类型：
--
作者：
Alberto, WD;Del Pilar, DM;De Los Angeles, BM
我们报告了一个比较研究，使用三种不同的化学计量学技术，以评估在苏基亚河水质的空间和时间的变化，特别强调使用判别分析这种评价所获得的改善。我们已经监测了22个参数在不同的站从上游，中部和下游流域开始在至少两年，包括232个不同的样品。我们得到了一个复杂的数据矩阵，这是使用聚类分析（CA），因子分析/主成分（FA/PCA）的模式识别技术处理。判别分析（DA）。CA呈现良好的结果作为第一个探索性的方法来评估空间和时间的差异，但它未能显示这些差异的细节。FA/PCA需要13个参数来指出71%的时间和空间变化;因此，在这种情况下，FA/PCA的数据减少并不像预期的那样可观。然而，FA/PCA允许组选定的参数，根据共同的功能，以及评估的发病率，每组对水质的整体变化，特别是在时间变化的分析。在时间和空间分析中，DA技术显示出最好的数据简化和模式识别结果。DA呈现一个重要的数据减少使用6个参数，提供87%的权利分配在时间分析。此外，在四个不同流域的空间分析中，它仅使用5个参数就产生了75%的权利分配。DA使我们能够大大降低初始数据矩阵的维数，指出一些参数，这些参数表明水质的最大变化以及与季节变化，城市径流和污染源相关的变化模式，为水质评估提供了一种新的方法。(C)2001爱思唯尔科技有限公司版权所有。
We report a comparative study using three different chemometric techniques to evaluate both spatial and temporal changes in Suquia River water quality, with a special emphasis on the improvement obtained using discriminant analysis for such evaluation. We have monitored 22 parameters at different stations from the upper, middle, and beginning of the lower river basin during at least two years including 232 different samples. We obtained a complex data matrix, which was treated using the pattern recognition techniques of cluster analysis (CA), factor analysis/principal components (FA/PCA). and discriminant analysis (DA). CA renders good results as a first exploratory method to evaluate both spatial and temporal differences, however it fails to show details of these differences. FA/PCA needs 13 parameters to point out 71% of both temporal and spatial changes; consequently data reduction from FA/PCA in this case is not as considerable as expected. However, FA/PCA allows to group the selected parameters according to common features as well as to evaluate the incidence of each group on the overall change in water quality, specially during the analysis of temporal changes. DA technique shows the best results for data reduction and pattern recognition during both temporal and spatial analysis. DA renders an important data reduction using 6 parameters to afford 87% right assignations during temporal analysis. Besides, it uses only 5 parameters to yield 75% right assignations during the spatial analysis of four different basin areas. DA allowed us to greatly reduce the dimensionality of the starting data matrix, pointing out to a few parameters that indicate the biggest changes in water quality as well as variation patterns associated with seasonal variations, urban run-off, and pollution sources, presenting a novel approach for water quality assessments. (C) 2001 Elsevier Science Ltd. All rights reserved.