  In a desired environmental protection system, groundwater may not be
excluded. In addition to the problem of over-exploitation, in total
disagreement with the concept of sustainable development, another not
negligible issue concerns the groundwater contamination. Mainly, this aspect is
due to intensive agricultural activities or industrialized areas. In
literature, several papers have dealt with transport problem, especially for
inverse problems in which the release history or the source location are
identified. The innovative aim of the paper is to develop a data-driven model
that is able to analyze multiple scenarios, even strongly non-linear, in order
to solve forward and inverse transport problems, preserving the reliability of
the results and reducing the uncertainty. Furthermore, this tool has the
characteristic of providing extremely fast responses, essential to identify
remediation strategies immediately. The advantages produced by the model were
compared with literature studies. In this regard, a feedforward artificial
neural network, which has been trained to handle different cases, represents
the data-driven model. Firstly, to identify the concentration of the pollutant
at specific observation points in the study area (forward problem); secondly,
to deal with inverse problems identifying the release history at known source
location; then, in case of one contaminant source, identifying the release
history and, at the same time, the location of the source in a specific
sub-domain of the investigated area. At last, the observation error is
investigated and estimated. The results are satisfactorily achieved,
highlighting the capability of the ANN to deal with multiple scenarios by
approximating nonlinear functions without the physical point of view that
describes the phenomenon, providing reliable results, with very low
computational burden and uncertainty.
