Application of fuzzy inference system in the prediction of air quality index / Nor Hayati Shafii ... [et al.]

• Main Authors: Shafii, Nor Hayati, Mohd Ramle, Nur Aini, Alias, Rohana, Md Nasir, Diana Sirmayunie, Fauzi, Nur Fatihah
• Format: Article
• Language: English
• Published: UiTM Cawangan Perlis 2021
• Subjects: Fuzzy logic; Air pollution and its control
• Online Access: https://ir.uitm.edu.my/id/eprint/60442/1/60442.pdf; https://ir.uitm.edu.my/id/eprint/60442/; https://crinn.conferencehunter.com/
• Institution: Universiti Teknologi Mara

Air pollution is the presence of substances in the atmosphere that are harmful to the health of humans and other living beings. It is caused by solid and liquid particles and certain gases that are suspended in the air. The air pollution index (API) or also known as air quality index (AQI) is an indicator for the air quality status at any area. It is commonly used to report the level of severity of air pollution to public and to identify the poor air quality zone. The AQI value is calculated based on average concentration of air pollutants such as Particulate Matter 10 (PM10), Ozone (O3), Carbon Dioxide (CO2), Sulfur Dioxide (SO2) and Nitrogen Dioxide (NO2). Predicting the value of AQI accurately is crucial to minimize the impact of air pollution on environment and human health. The work presented here proposes a model to predict the AQI value using fuzzy inference system (FIS). FIS is the most well-known application of fuzzy logic and has been successfully applied in many fields. This method is proposed as the perfect technique for dealing with environmental well known and tackling the choice made below uncertainty. There are five levels or indicators of AQI, namely good, moderate, unhealthy, very unhealthy, and hazardous. This measurement is based on classification made from the Department of Environment (DOE) under the Ministry of Science, Technology, and Innovation (MOSTI). The results obtained from the actual data are compared with the results from the proposed model. With the accuracy rate of 93%, it shows that the proposed model is meeting the highest standard of accuracy in forecasting the AQI value.