There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
In this study a thorough analysis is conducted concerning the prediction of groundwater
levels of Ljubljana polje aquifer. Machine learning methodologies are implemented
using strongly correlated physical parameters as input variables. The results show
that data-driven modelling approaches can perform sufficiently well in predicting
groundwater level changes. Different evaluation metrics confirm and highlight the
capability of these models to catch the trend of groundwater level fluctuations. Despite
the overall adequate performance, further investigation is needed towards improving
their accuracy in order to be comprised in decision making processes.