Groundwater Quality Assessment: An Improved Approach to K-Means Clustering, Principal Component Analysis and Spatial Analysis: A Case Study

被引:66
作者
Marin Celestino, Ana Elizabeth [1 ,2 ]
Martinez Cruz, Diego Armando [3 ]
Otazo Sanchez, Elena Maria [2 ]
Gavi Reyes, Francisco [4 ]
Vasquez Soto, David [5 ]
机构
[1] CONACYT Inst Potosino Invest Cient & Tecnol AC, Div Geociencias Aplicadas, Camino Presa San Jose 2055, San Luis Potosi 78216, San Luis Potosi, Mexico
[2] Univ Autonoma Estado Hidalgo, Area Acad Quim, Carretera Pachuca Tulancingo Km 4-5, Mineral De La Reforma 42184, Hidalgo, Mexico
[3] CONACYT Ctr Invest Mat Avanzados SC, Calle CIMAV 110,Col 15 Mayo Tapias, Durango 34147, Durango, Mexico
[4] Colegio Postgrad, Postgrad Hidrociencias, Carr Fed Mexico Texcoco Km 36-5, Texcoco 56230, Estado De Mexic, Mexico
[5] Colegio Mexicano Especialistas Recursos Nat, Callejon Flores 8, Texcoco 56220, Estado De Mexic, Mexico
关键词
K-means clustering; PCA; spatial analysis; water quality; hydrogeochemical; coastal aquifer; MULTIVARIATE STATISTICAL TECHNIQUES; SURFACE-WATER QUALITY; RIVER-BASIN; AQUIFERS; ALGORITHM; REGION; CHINA; ZONES; INDIA;
D O I
10.3390/w10040437
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
K-means clustering and principal component analysis (PCA) are widely used in water quality analysis and management. Nevertheless, numerous studies have pointed out that K-means with the squared Euclidean distance is not suitable for high-dimensional datasets. We evaluate a methodology (K-means based on PCA) for water quality evaluation. It is based on the PCA method to reduce the dataset from high dimensional to low for the improvement of K-means clustering. For this, a large dataset of 28 hydrogeochemical variables and 582 wells in the coastal aquifer are classified with K-means clustering for high dimensional and K-means clustering based on PCA. The proposed method achieved increased quality cluster cohesion according to the average Silhouette index. It ranged from 0.13 for high dimensional k-means clustering to 5.94 for K-means based on PCA and the practical spatial geographic information systems (GIS) evaluation of clustering indicates more quality results for K-means clustering based on PCA. K-means based on PCA identified three hydrogeochemical classes and their sources. High salinity was attributed to seawater intrusion and the mineralization process, high levels of heavy metals related to domestic-industrial wastewater discharge and low heavy metals concentrations were associated with industrial wastewater punctual discharges. This approach allowed the demarcation of natural and anthropogenic variation sources in the aquifer and provided greater certainty and accuracy to the data classification.
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页数:21
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