Air quality index prediction for clearer skies using improved long short-term memory

Abstract

Air pollution has become an international calamity, a problem for human health and the environment. The ability to predict the air quality becomes a crucial task. The usual approaches for assessing air quality are exhausted when extracting complicated non-linear relationships and long-term dependence features embedded in the data. Long- and short-term memory, a recurrent neural network family, has emerged as a potent tool for addressing the mentioned issues, so computer-aided technology has become essential to aid with a high level of prediction and best-in-class accuracy. In this study, we investigated classic time-series analysis based on Improved Long short-term memory (ILSTM) to improve the performance of air quality index prediction. The predicted AQI value for the 25 days lies in a 97.63% Confidence interval zone and highly adoptable performance metrics such as R-Square, MSE, RMSE, and MAE values.