Framework for clustering groundwater quality using Self-Organizing Maps to improve aquifer monitoring and management: a case study of the Gabros de Beja aquifer system, Portugal

A new clustering strategy was developed and tested using Self-Organizing Maps (SOM), an unsupervised Artificial Neural Network (ANN) type, for identifying zones with similar contamination characteristics within an aquifer. The Gabros de Beja aquifer system (GBAS), located in the Alentejo region, Portugal, was selected as a case study due to its vulnerability to diffuse pollution from intensive agriculture. The proposed methodology consists of: (a) selection of the most representative groundwater contaminants in the aquifer area (i.e., nitrates, sulfates and chlorides); (b) determination of the Natural Background Level (NBL) of the selected groundwater compounds; (c) computation of the ratio between the median concentrations of the groundwater compounds being analyzed and their respective NBL concentration; and finally, (d) application of the SOM clustering technique to group homogenous contaminated areas within the aquifer. The NBL illustrates what thresholds are likely signs of anthropogenic effect by indicating how high or low a parameter's value would be expected under natural geogenic conditions and therefore was used as a first normalization of the dataset. For this methodology, the NBL was computed as the 90th percentile concentration of the selected compounds in piezometers within the study area that presented a median nitrate concentration smaller than 10 mg/L. Nitrate, sulfate and chloride concentration medians from 45 piezometers were used. The results show that the SOM network classified the piezometers into six classes (CL1 to CL6). The least contaminated clusters were CL1 (8) and CL4 (17), with all three compounds presenting median concentrations around 50 mg/L, which for nitrate is the threshold for drinking water limits. CL5 (5) reached median nitrate concentrations above 100 mg/L, while chlorides and sulfates remained below 50 mg/L. CL2 (6) showed an increase in chloride concentration to 100 mg/L, with the other two compounds' concentrations below 65 mg/L. CL3 (3) presented the highest salinization levels reaching chloride concentrations above 180 mg/L, with sulfates around 80 mg/L and nitrates around 50 mg/L. Finally, CL6 (6) presented median levels of the three compounds above 80 mg/L. The most contaminated groups (CL3, CL5 and CL6) were present in sedimentary and weathered metamorphic lithologies, which present high hydraulic conductivities, coinciding either with urban or agricultural areas associated with large-scale irrigation schemes, reinforcing the anthropogenic source of the contaminants. Hence, this study presented a clustering framework that, by reducing the dimensionality of the original dataset, helps to establish a priority list of polluted areas with different degrees of contamination, which is indeed essential for implementing monitoring and management measures for attenuating groundwater pollution.