Uncertainty due to spatial and temporal variability of different pollutants
found in soil, water and air is an important issue in the modelling of
environmental impacts. Recognition of spatial environmental patterns is
relatively straightforward to achieve by applying most of the well-known
artificial neural networks or adopting a stochastic approach. However, it
is the temporal component of the environmental variables that makes
modelling of the environmental pollutants particularly complicated and
challenging. This paper presents the results of a case study investigating
the artificial neural network performance when modelling the
spatio-temporal behaviour of environmental pollutants. The test case
involved the modelling and prediction of the distribution of water quality
indicators along a river from the point of discharge of treated mine
effluents. The behaviour of river water quality indicators is rather
complex and shows a lot of fluctuations due to a number of
hydrochemical, hydrobiological and hydrodynamic factors. Results
obtained in this study indicate that artificial neural networks provide a
very powerful and robust method for simultaneous modelling of
spatio-temporal behaviour of environmental pollutants.