Research Article | Open Access
Predicting Air Quality Index Values Using ARIMA Model
Dr.S.Meenakshi Sundaram, Dr. P. Sivakumar, Dr. M. Parthiban, Dr.U.Revathy
Pages: 2557-2569
Abstract
Air pollution is a serious issue that has negative consequences for human health and living situations. As a result, pollution levels must be monitored in order to keep people informed about the air quality. This is accomplished through the use of an index known as the Air Quality Index AQI, which converts the concentrations of multiple contaminants into a single number. Government offices utilize an air quality list AQI to illuminate the general population about how defiled the air is currently or will become from here on out. The AQI is determined by averaging readings from an air quality sensor, which can rise attributable to traffic, woodland fires, or different elements that add to air contamination. As the AQI rises, general wellbeing concerns ascend too, hurting youngsters, the old, and those with respiratory or cardiovascular issues. An air contamination fixation during a characterized averaging period, gathered from an air screen or model, is expected for AQI computation. The work used to switch from air poison focus over completely to AQI fluctuates by poison, while the capacity used to change from air toxin focus over completely to AQI changes by toxin. The upsides of its air quality file are normally ordered into ranges
Keywords
Machine Learning, ARIMA,K-Means, AQI, RMSE, AR.