In the present study, the changes in the groundwater quality in a Hungarian settlement,
Báránd, were examined, nine years after the construction of a sewerage network. The
sewerage network in the study area was completed in 2014, with a household connection
rate exceeding 97% in 2023. In the summer of 2023, water samples were taken from 37
dug groundwater wells. Changes in the water quality were assessed using three water
quality indicators (the Water Quality Index (WQI), Contamination degree (Cd), and
Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI)) and
geographic information (GIS), data visualization systems, and artificial intelligence
(AI). During the evaluation of the quality of the groundwater, eight water chemical
parameters were used (pH, EC, NH4+, NO2−, NO3−, PO43−, COD, Na+). Based on interpolated
maps and water quality indices, it was established that while an increasing portion
of the area exhibits adequate or good water quality compared to the pre-sewerage period,
a deterioration has occurred relative to recent years. Even nine years after the sewerage
network construction, elevated concentrations of inorganic nitrogen forms and organic
matter persist, indicating the continued presence of accumulated pollutants, as confirmed
by all three water quality indicators to varying degrees and spatial patterns. The
interactive data visualization and cloud-based sharing of the data of the water quality
geodatabase were made freely available with the help of Tableau Public. A Feed-Forward
Neural Network (FFNN) was developed to predict the groundwater quality, estimating
the water quality statuses of three water quality indicators based on water chemistry
parameters. The results showed that the applied training algorithms and activation
functions proved to be the most effective in the case of different network structures.
The most accurate prediction of the WQI and CCME WQI indicators was provided by the
Bayesian control algorithm (trainbr), which achieved the lowest mean-squared error
(RMSEWQI = 0.1205, RMSECCME WQI = 0.1305) and the highest determination coefficient
(R2WQI = 0.9916, R2CCME WQI = 0.9838). For the Cd index, the accuracy of the model
was lower (RMSE = 0.1621, R2 = 0.9714), suggesting that this indicator is more difficult
to predict. With regard to our study, it should be emphasized that data visualization
is a particularly practical tool for the post-processing of spatial monitoring data,
as it is suitable for displaying information in an intuitive, visual form, for discovering
spatial patterns and relationships, and for performing real-time analyses. AI is expected
to further increase visualization efficiency in the future, enabling the rapid processing
of large amounts of data and spatial databases, as well as the identification of complex
patterns.
