Coastal groundwater pattern recognition supported by cluster analysis

In coastal zones, groundwater overexploitation reduces freshwater outflow to the sea and causes seawater to migrate toward fresh groundwater resources, increasing salinity in groundwater reservoirs. This seawater intrusion is among the world's leading causes of groundwater pollution, as salty water can affect safe drinking consumption, food production, and ecosystem services. To explore this and others contaminations sources, cluster analysis has been used for decades to aid in water resource pattern recognition in coastal aquifers around the world. 

This work shows how cluster analysis has been applied for seawater intrusion pattern recognition in coastal zonas around the world between 2000 and 2022 through a systematic review based on the PRISMA statement. After the searching and selection stages, it was carried out the bibliometric analysis of the 81 identified studies. Furthermore, it was discussed information about the number of samples, number of variables, redundant variables, sample density per area, sample density per variable, clustering principal features, limitations for sources differentiation, assembly between methods, software, and pre-processing strategies. 

The identified methods were hierarchical clustering analysis (HCA), K-means clustering, Fuzzy C-means, and self-organizing maps (SOM). While 56 studies applied Q-mode for grouping water samples with similar characteristics, 17 applied R-mode for grouping variables, and 8 applied both modes. The preferred method was HCA with Ward´s linkage and Euclidean distance, but many studies didn’t specify the linkage or the distance criteria.  Of those studies that applied Q-mode, 77% associated at least one cluster with the influence of seawater intrusion. On the other hand, this work shows that 58% of the reviewed studies did not report raw data, which presents issues for validation, replication, and socialization of the results.