Evaluation of Time Series Forecasting Models for Estimation of PM2.5 Levels in Air
This work addresses air pollution monitoring for environmentalists and researchers, but it is incremental as it applies existing models to a specific dataset.
This study tackled the problem of estimating PM2.5 levels in air using time series forecasting models, finding that LSTM outperformed other models like ARIMA and FBProphet in terms of mean absolute percentage error, while all methods gave comparable results for average root mean squared error.
Air contamination in urban areas has risen consistently over the past few years. Due to expanding industrialization and increasing concentration of toxic gases in the climate, the air is getting more poisonous step by step at an alarming rate. Since the arrival of the Coronavirus pandemic, it is getting more critical to lessen air contamination to reduce its impact. The specialists and environmentalists are making a valiant effort to gauge air contamination levels. However, its genuinely unpredictable to mimic subatomic communication in the air, which brings about off base outcomes. There has been an ascent in using machine learning and deep learning models to foresee the results on time series data. This study adopts ARIMA, FBProphet, and deep learning models such as LSTM, 1D CNN, to estimate the concentration of PM2.5 in the environment. Our predicted results convey that all adopted methods give comparative outcomes in terms of average root mean squared error. However, the LSTM outperforms all other models with reference to mean absolute percentage error.