  Industrial pollution is often considered to be one of the prime factors
contributing to air, water and soil pollution. Sectoral pollution loads
(ton/yr) into different media (i.e. air, water and land) in Lagos were
estimated using Industrial Pollution Projected System (IPPS). These were
further studied using Artificial neural Networks (ANNs), a data mining
technique that has the ability of detecting and describing patterns in large
data sets with variables that are non- linearly related. Time Lagged Recurrent
Network (TLRN) appeared as the best Neural Network model among all the neural
networks considered which includes Multilayer Perceptron (MLP) Network,
Generalized Feed Forward Neural Network (GFNN), Radial Basis Function (RBF)
Network and Recurrent Network (RN). TLRN modelled the data-sets better than the
others in terms of the mean average error (MAE) (0.14), time (39 s) and linear
correlation coefficient (0.84). The results showed that Artificial Neural
Networks (ANNs) technique (i.e., Time Lagged Recurrent Network) is also
applicable and effective in environmental assessment study. Keywords:
Artificial Neural Networks (ANNs), Data Mining Techniques, Industrial Pollution
Projection System (IPPS), Pollution load, Pollution Intensity.
