Particulate matter prediction using ANFIS modelling techniques

Recent studies on air pollution emphasized particulate matter impact on human health and climate changes. This impact generated a trend for developing research projects which deal with monitoring and forecasting air quality. This paper fits into this trend and presents an ANFIS (adaptive neuro-fuzzy inference system) modelling approach to predict particulate matter concentration for short terms. The ANFIS technique was tested for three data sets covering all seasons specific to urban areas from Romania. The data sets type imposed the fuzzy inference system generating method and the optimization method. The resulted prediction model can be used to warn the population when the PM concentration exceeds standard limits, and also to extract useful data for knowledge-based modelling.