Abstract
Groundwater is a vital natural resource. Water from this source is used practically worldwide because of its economical price, easy access, reliability, and excellent quality. However, groundwater contamination might make it unsafe to use for agriculture, household, and drinking needs. To solve groundwater management issues, groundwater system modelling is a fundamental requirement. Adopting any cleanup technique requires the identification of undiscovered sources of pollution. The data required for the processes involved in developing Artificial Neural Networks (ANN) models is produced by a groundwater flow and transport simulation model. The concentration breakthrough curves are described by the suggested approach using statistical parameters such as skewness, kurtosis, maximum value, average value, and standard deviation. To identify the sources in terms of their location, magnitudes, and duration of activity, a feed-forward multilayer artificial neural network (ANN) is employed with the defined parameters. Test and training patterns are used in varying numbers during experiments. To determine the source fluxes of groundwater pollution, a developed methodology is illustrated using example problems. To solve the groundwater flow and transport simulation model, specific boundary conditions and input parameters may be needed. Uncertainty in one is understanding of the given data, or epistemic uncertainty, can result in imprecise input parameters via indirect measurements and subjective interpretation. Using the Fuzzy Vertex Method, the epistemic uncertainty in the flow parameters is characterized as a result of imperfect measurement of source flux and one of the flow parameters.