||<p>Assessing the quality of groundwater in a given aquifer can be an expensive and
time-consuming process. An effort is made in this thesis to predict several water
quality parameters, namely Fe, Cl, SO4, and total hardness (as CaCO3), from the easily
measured properties total dissolved solids (TDS) and electrical conductivity (EC).
This is achieved by establishing functional relationships between the four quality
parameters, TDS, and EC using multiple linear regression (MLR) and artificial neural
network (ANN) models. Data for these variables were gathered from five unrelated groundwater
quality studies. Results indicate that the ANN models produced more accurate functions
than MLR, showcasing the strength of ANN’s in predictive applications. Analysis of
the relative importance of each parameter illustrates that total hardness (CaCO3)
is most influential in determining TDS, while sulphate is most influential on EC.
These results could have a significant impact on the future of groundwater quality