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Title

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

Year: 1396
COI: JR_IJE-30-11_016
Language: EnglishView: 181
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Authors

A Safari - Department of Computer Engineering, Shahr Qods Branch, Islamic Azad University, Tehran, Iran
R Hosseini - Department of Computer Engineering, Shahr Qods Branch, Islamic Azad University, Tehran, Iran
M Mazinani - Department of Electronic Engineering, Shahr Qods Branch, Islamic Azad University, Tehran, Iran

Abstract:

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

Keywords:

Fuzzy Logic , Type-2 Fuzzy Set , Adaptive Neuro Fuzzy Inference System , Air Pollution Disaster ,

Paper COI Code

This Paper COI Code is JR_IJE-30-11_016. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

https://civilica.com/doc/719520/

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Safari, A and Hosseini, R and Mazinani, M,1396,A Novel Type-2 Adaptive Neuro Fuzzy Inference System Classifier for Modelling Uncertainty in Prediction of Air Pollution Disaster,https://civilica.com/doc/719520

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Scientometrics

The specifications of the publisher center of this Paper are as follows:
Type of center: Azad University
Paper count: 1,933
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