A hybrid learning algorithm for Interval Type-2 Fuzzy Neural Networks in time series prediction for the case of air pollution

Abstract

Air pollution is a growing problem arising from domestic heating, high density of vehicle traffic, electricity production, and expanding commercial and industrial activities, all increasing in parallel with urban population. Monitoring and forecasting of air quality index in urban areas is important due to health impact. Hybrid intelligent techniques are successfully used in modeling of highly complex and non-linear phenomena. In this paper, interval type-2 fuzzy neural network (IT2FNN) hybrid method has been proposed to predict the impact of meteorological pollutants on ozone (O3) over an urban area. The IT2FNN model forecasts trends in O3 with high performance.