Air Quality Prediction and Monitoring Using Machine Learning-Based Forecasting Approach

Air pollution is one of the largest dangers to the environment and public health today. As a result, the ecology, the climate, and human health all suffer. Numerous techniques for monitoring air pollution have been tried and refined over time. The monitoring of air pollution, a prediction method, and actions taken to decrease its effects are all examined in this research. Additionally, the forecasting model has been used to forecast the concentration of polluting gases in the future. Using a machine learning model called Autoregressive Integrated Moving Average (ARIMA), we analyze the air pollution data obtained from the multisensory device in this research and use it to forecast air pollution in the future. A multimodal air quality device with five metal oxide chemical sensors inside has generated 9358 instances of hourly averaged data for the collection. It was located in a particularly dirty part of the city, on a field at street level. The dataset being considered has a public version in the UCI machine learning repository. We first pre-processed the dataset, then examined the air pollution caused by various chemicals, and lastly, we developed a model for calculating the concentration of various gases by using various machine learning methods. Finally, we created an ARIMA model to forecast future gas concentrations. Traditional time series models and machine learning techniques are used to anticipate air pollution in the future. We also calculated the accuracy of the ARIMA model and discovered 0.834 RMSE, 0.109 MSE, and 0.646 MAE.