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Air Quality Index and Public Health: Modelling Using Fuzzy Inference System
Current Issue
Volume 1, 2014
Issue 4 (September)
Pages: 85-89   |   Vol. 1, No. 4, September 2014   |   Follow on         
Paper in PDF Downloads: 21   Since Aug. 28, 2015 Views: 1488   Since Aug. 28, 2015
Authors
[1]
Bouharati Saddek, Laboratory of Intelligent Systems, Faculty of Technology, UFAS Setif1 University, Algeria; Faculty of Natural Sciences and Life, UFAS Setif1 University, Algeria.
[2]
Benzidane Chahra, Faculty of Natural Sciences and Life, UFAS Setif1 University, Algeria.
[3]
Braham-Chaouch Wafa, Research Unit Renewable Saharan Environments (URER. MS), Adrar, Algeria.
[4]
Boumaïza Souad, Faculty of Natural Sciences and Life, UFAS Setif1 University, Algeria.
Abstract
The Air Quality Index (AQI) is divided into some categories indicating increasing levels of health concern. An AQI value over certain threshold represents hazardous air quality whereas, if it is below a certain value, the air quality is good. Each country or continent established its standards and limits as color code corresponding to a range of index values (very low, low, medium, high and very high). Each color matches the effect of air pollution on a category of sensitive population when this pollution is likely to be affected. The problem is that the limits on these index values are sharp and characterized by their uncertainty and imprecision. For the effect on a population group is very complex to predict accurately. It depends from one person to another even it belongs to the same category of classification. It is not that from a unit value index to switch from one color to another such category of people will be affected and the other is no longer relevant. In this study, we find that the transition between ranges colors normally is fuzzy due to their incertitude effect on levels of health population concern. We found it useful to have analytical techniques based on artificial intelligence, especially the principles of fuzzy logic tool. The use of the Fuzzy inference system, demonstrate his capability for addressing the complex problems of uncertainty data. The FIS model was structured to prevent the nature of risk disease according the AQI values in inputs of system.
Keywords
Air Pollution, Air Quality, Artificial Intelligence, Fuzzy Logic
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