Air Quality Modelling by Kohonen’s Self-organizing Feature Maps and LVQ Neural Networks

The paper presents a design of parameters for air quality modelling and the classification of districts into classes according to their pollution. Further, it presents a model design, data pre-processing, the designs of various structures of Kohonen’s Self-organizing Feature Maps (unsupervised methods), the clustering by K-means algorithm and the classification by Learning Vector Quantization neural networks (supervised methods). Therefore, the model generates well-separated clusters and has good generalization ability as well. Key-Words: Air quality, modelling, Kohonen’s self-organizing feature maps, K-means algorithm, Learning Vector Quantization neural networks, classification.