Evaluation of Time Series Forecasting Models for Estimation of PM2.5 Levels in Air

TL;DR

This study compares traditional and deep learning models for predicting PM2.5 air pollution levels, finding that LSTM models outperform others in accuracy despite similar average errors.

Contribution

It evaluates and compares ARIMA, FBProphet, LSTM, and 1D CNN models for PM2.5 prediction, highlighting LSTM's superior performance.

Findings

LSTM achieves the lowest mean absolute percentage error.

All models show similar average root mean squared error.

Deep learning models can effectively predict air pollution levels.

Abstract

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.