A Substantial Approach to Predict Air Quality Using LVQ Neural Network

Nowadays, air pollution is one of the major threats to the ecosystem and human existence. Addressing air pollution is the biggest environmental challenge in smart city environments. Therefore, monitoring of air quality has become an important task in urban and industrial areas. The primary focus to monitor air quality is a human-centred approach, as data collected from different monitoring stations are evaluated based on human experience and unreliable. Therefore, the emergence of machine learning technologies such as decision trees, neural networks (NN), and support vector machine (SVM) justifies the application of statistical methods to environmental modelling, especially in the forecasting of air quality. In addition, the existing techniques are insufficient to find the impact of every air pollutants for minimising air pollutions. An Artificial Neural Network (ANN) with Learning Vector Quantisation (LVQ) is implemented for prediction in this research study. The results showed that the ANN-LVQ achieved 97.58% of accuracy with a minimal error rate.