A Novel Type-2 ANFIS Classifier for Modelling Uncertainty in Prediction of Air Pollution Disaster (RESEARCH NOTE) Article in Press

The type-2 fuzzy set theory is one of the most powerful tools for dealing with uncertainty and imperfection in a dynamic and complex environment. The applications of type-2 fuzzy sets and soft computing methods are rapidly emerging in the ecological fields such as air pollution and weather prediction. The air pollution problem is a major public health problem in many cities of the world. Prediction of natural phenomena always suffers from uncertainty in the environment and incompleteness of data. There are many studies reported for air quality index prediction but all of them suffers from uncertainty and imprecision associated to the incompleteness of knowledge and imprecise input measures. This work takes advantages of learning and adoption of adaptive neural networks alongside in new environment. Furthermore, it presents an adaptive neuro-type-2 fuzzy inference system (ANT2FIS) to address the uncertainty and imprecision in air quality prediction. The dataset of this study was collected from Tehran municipality official website for last five years (2012-2017). The result reveals that the ANT2FIS method prediction is more reliable and is capable of handling uncertainty compared to the other counterpart methods. The performance results on real dataset shows advantage of the type2- ANFIS system in the prediction process with an average accuracy of 94% (AUC 99%) compared to other related works.