• P.S.Gayathri
• R.Krishna Sharma
• S.Nagaveena

Air pollution is a major environmental concern since it affects both human well-being and the ecosystem's natural balance. Developing mitigation strategies and legislation demands a precise estimation of air pollution levels. Traditional prediction models are frequently unable to accurately capture the complexity and unpredictability of environmental data. In response, this research proposes a fuzzy logic-based approach to predict air pollution. Fuzzy logic offers a framework for managing ambiguous and imprecise information by combining linguistic considerations with specialized knowledge. This study uses fuzzy logic to construct prediction models for significant air pollutants such as nitrogen dioxide (NO2) and surface ozone (O3). Using inputs such as meteorological data, this approach forecasts pollutant concentrations using fuzzy inference techniques. The usefulness of the proposed fuzzy logic models is evaluated using real data collected from Kanyakumari monitoring station. The findings show that fuzzy logic has the potential to be a reliable method for predicting air quality, giving researchers, environmental groups, and lawmakers involved in air pollution management information. By increasing forecast accuracy and taking uncertainty into account, fuzzy logic promotes preventative measures that enhance air quality and public health.

"Fuzzy Analysis For Short Term Forecast Of Ozone Levels At a Semi-Urban Site ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.e422-e428, October-2024, Available :http://www.jetir.org/papers/JETIR2410445.pdf

"Fuzzy Analysis For Short Term Forecast Of Ozone Levels At a Semi-Urban Site ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppe422-e428, October-2024, Available at : http://www.jetir.org/papers/JETIR2410445.pdf