Simulation and optimization of genetic algorithm-artificial neural network based air quality estimator

Abstract

In this work intelligent model for estimation of the concentration of carbon monoxide in a polluted environment is developed on mat Lab platform. The results are validated using data collected from repository linked to University of California. The data records are over the duration of one year using E nose sensor placed in main city of Italy. The records are rectified and segmented at different length to extract the base and divergence values features. An artificial neural network model (ANN) is developed and the result is validated manually. Another optimized genetic algorithm-artificial neural network based air quality estimation model is developed which validate the result using artificial intelligence technique to get a better performance network.