Estimating Sectoral Pollution Load in Lagos, Nigeria Using Data Mining Techniques
This work addresses environmental assessment for policymakers in Lagos, but it is incremental as it applies existing neural network methods to a specific dataset.
The study tackled estimating sectoral pollution loads in Lagos, Nigeria by applying data mining techniques, specifically comparing neural network models, and found that the Time Lagged Recurrent Network performed best with a mean average error of 0.14 and a linear correlation coefficient of 0.84.
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.