The project will assess the application of artificial neural networks to model the water quality response function of the mean sea level aquifer systems. The model will predict the quality of the water from public water groundwater abstraction stations through a number of input and output variables. The model will be calibrated through the use of real time data collected from public abstraction stations to ensure that the input-output function developed through model is progressively improved to reflect the real response of the aquifer system. The predictive capabilities of the model can allow for the introduction of an adaptive groundwater abstraction strategy for the mean sea level aquifer to achieve a better water supply quality (lower levels of salinity) without increasing the levels of production.
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