A Novel Type-2 Adaptive Neuro Fuzzy Inference System Classifier for Modelling Uncertainty in Prediction of Air Pollution Disaster (RESEARCH NOTE)


1 Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

2 Department of Electronic Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran


Type-2 fuzzy set theory is one of the most powerful tools for dealing with the uncertainty and imperfection in dynamic and complex environments. 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. However, various studies have been reported for prediction of the air quality index but all of them suffer from uncertainty and imprecision associated to the incompleteness of knowledge and imprecise input measures. This article takes advantages of learning 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 data set of this study was collected from Tehran municipality official website for the last five years (2012-2017). The results reveal that the ANT2FIS prediction method is more reliable and is capable of handling uncertainty compared to the other counterpart methods. The performance results on real data set show the superiority of the ANT2FIS model in the prediction process with an average accuracy of 94% (AUC 99%) compared to other related works. These results are promising for early prediction of the natural disasters and prevention of its side effects.


1.     Sher, E., "Handbook of air pollution from internal combustion engines: Pollutant formation and control, Academic Press,  (1998).
2.     Hosseini, R., Ellis, T., Mazinani, M. and Dehmeshki, J., "A genetic fuzzy approach for rule extraction for rule-based classification with application to medical diagnosis", in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Citeseer., (2011), 05-09.
3.     Sadeghian, A., Mendel, J. and Tahayori, H., "Advances in type-2 fuzzy sets and systems: Theory and applications", Springer,  Vol. 301,  (2013), ISBN: 978-1-4614-6666-6.
4.     Hosseini, R., Qanadli, S.D., Barman, S., Mazinani, M., Ellis, T. and Dehmeshki, J., "An automatic approach for learning and tuning gaussian interval type-2 fuzzy membership functions applied to lung cad classification system", IEEE Transactions on Fuzzy Systems,  Vol. 20, No. 2, (2012), 224-234.
5.     Mendel, J., Hagras, H., Tan, W.-W., Melek, W.W. and Ying, H., "Introduction to type-2 fuzzy logic control: Theory and applications, John Wiley & Sons,  (2014).
6.     Sowlat, M.H., Gharibi, H., Yunesian, M., Mahmoudi, M.T. and Lotfi, S., "A novel, fuzzy-based air quality index (FAQI) for air quality assessment", Atmospheric Environment,  Vol. 45, No. 12, (2011), 2050-2059.
7.     Yildirim, Y. and Bayramoglu, M., "Adaptive neuro-fuzzy based modelling for prediction of air pollution daily levels in city of zonguldak", Chemosphere,  Vol. 63, No. 9, (2006), 1575-1582.
8.     Singh, K.P., Gupta, S., Kumar, A. and Shukla, S.P., "Linear and nonlinear modeling approaches for urban air quality prediction", Science of the Total Environment,  Vol. 426, (2012), 244-255.
9.     Ababneh, M.F., Ala’a, O. and Btoush, M.H., "Pm10 forecasting using soft computing techniques", Research Journal of Applied Sciences, Engineering and Technology,  Vol. 7, No. 16, (2014), 3253-3265.
10.   Shahraiyni, H.T., Sodoudi, S., Kerschbaumer, A. and Cubasch, U., "A new structure identification scheme for anfis and its application for the simulation of virtual air pollution monitoring stations in urban areas", Engineering Applications of Artificial Intelligence,  Vol. 41, (2015), 175-182.
11.   Elangasinghe, M., Singhal, N., Dirks, K., Salmond, J. and Samarasinghe, S., "Complex time series analysis of pm 10 and pm 2.5 for a coastal site using artificial neural network modelling and k-means clustering", Atmospheric Environment,  Vol. 94, (2014), 106-116.
12.   Shad, R., Mesgari, M.S. and Shad, A., "Predicting air pollution using fuzzy genetic linear membership kriging in gis", Computers, Environment and Urban Systems,  Vol. 33, No. 6, (2009), 472-481.
13.   Moghadam-Fard, H. and Samadi, F., "Active suspension system control using adaptive neuro fuzzy (anfis) controller", International Journal of Engineering-Transactions C: Aspects,  Vol. 28, No. 3, (2014), 396-401.
14.   Mendez, G. and De Los Angeles Hernandez, M., "Interval type-2 anfis", Innovations in Hybrid Intelligent Systems,  (2007), 64-71.
15.   Khezri, R., Hosseini, R. and Mazinani, M., "A fuzzy rule-based expert system for the prognosis of the risk of development of the breast cancer", International Journal of Engineering Transactions A: Basics,  Vol. 27, No. 10, (2014), 1557-1564.
16.   Bernard, S., Chatelain, C., Adam, S. and Sabourin, R., "The multiclass roc front method for cost-sensitive classification", Pattern Recognition,  Vol. 52, (2016), 46-60.
17.   "Air quality index", in U.S. Environmental Protection Agency (EPA), Office of Air Quality Planning and Standards Outreach and Information Division Research Triangle Park, Outreach and Information Division Research Triangle Park, NC., (2014 of Conference).
18.   Ara, A.L., Tolabi, H.B. and Hosseini, R., "Dynamic modeling and controller design of distribution static compensator in a microgrid based on combination of fuzzy set and galaxy-based search algorithm", International Journal of Engineering-Transactions A: Basics,  Vol. 29, No. 10, (2016), 1392-1400.