AQI Predication Using Temporal Data Mining

Abstract The Air Quality Index (AQI) is an air quality standards pointer based on air pollutants that have negative impacts on human health and the environment.Due to many human achievements, air pollution is increasing very rapidly and it is the introduction of chemicals, particles or organic materials into the atmosphere that harms the human environment and the natural environment.Indeed, air pollution in metropolitan and industrial cities is one of the major environmental problems. Therefore it is very important to predict pollution and avoid these problems.One of the most exciting and difficult functions is the forecast of air pollution using data mining. Many systems are intentionally help with data storage, inventory management, and convenient data creation.India's air quality indicator is a standard measure used to indicate pollution (so2, no2, rspm, spm, etc.) from time to time. The main objective of the current study is to estimate the temporal AQI used by the previous day AQI and to predict and visualize the temporal data mine using a slope interval and an arbitrary forecasting process of climate change. In Navigation Forecast, we divide the database into 85% data and 15% data based on data testing and training to determine seasonal variations and styles. Balancing problems are often exploited by problems and forecasting uses an arbitrary forecasting process and gradient idle time. Air quality forecast based on at least one year's forecast as a reliable slope using historical data of previous years and a persistent problem.