Affiliations: [a] Department of Computer Science and Engineering, GSFC University, Vadodara, Gujarat, India | [b] Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India | [c] Department of CSE, Alliance School of Advanced Computing, Alliance University, Bengaluru, India | [d] Department of ET and T, BIT Raipur, Chhattisgarh, India
Correspondence:
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Corresponding author: Nilesh Bhaskarrao Bahadure, Department of Computer Science and Engineering, GSFC University, Vadodara, Gujarat, India. E-mails: [email protected], [email protected].
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.
Keywords: LSTM (Long short-term memory), air quality forecasting, time series modeling, recurrent neural networks, hyperparameter tuning
