A new approach to pollution vulnerability assessment in aquifers using K-means analysis

Abstract

The most used methods to evaluate the vulnerability to contamination of aquifers are based on overlay index maps, such us DRASTIC, GOD and AVI. These methods assign weighting and rating values to hydrogeological characteristics, introducing subjectivity in the evaluation. In this research, a new methodology is proposed to eliminate some of that subjectivity. The methodology evaluates the vulnerability to contamination of a detrital aquifer using K-means cluster analysis with a new set of parameters. The set is composed of some parameters extracted from these methods, as well as other new ones that have a signi cant in uence on the movement of contaminants. Application of the Principal Components Analysis (PCA) technique before using K-means cluster allowed the selection of the relevant parameters. In order to validate the methodology, this was applied to a detrital aquifer located at central Spain (the so-called "Aluviales Jarama-Tajuña" aquifer) with a signi cant agricultural development. To compare the traditional methods of vulnerability assessment with the K-means cluster, nitrate concentration was used as a pollution indicator. Thus, 23 groundwater quality samples were used to correlate (Spearman´s correlation coe cient) the vulnerability values with nitrate concentration to validate the most suitable method. The results showed that GOD and AVI were not appropriate methods to evaluate the vulnerability of the aquifer, because they use few variables which do not represent relevant features for the vulnerability assessment. Alternatively, DRASTIC and K-means cluster analysis obtained higher Spearman´s correlation coe cients. The relevant features selected by PCA analysis were depth of groundwater (D), net recharge (R), and land use (L). The new proposed method grouped data in three clusters (low, moderate and high vulnerability), increasing the Spearman´s correlation from 34% of DRASTIC method to 48%. This result con rmed the advantage of applying cluster analysis in the assessment of groundwater vulnerability in detrital aquifers.

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“…where N p means that there are N p data in the cluster center c p , Q pu means the uTH data in the cluster center c p , and the iteration continues until it meets the termination condition that the sum of squared errors A (Formula (4)) converges [53]. Then we can obtain the final clustering center c 1 , c 2 ,.…”