Air Quality Index Prediction using Bi-LSTM

Abstract

Air quality prediction plays a vital role in safeguarding public health and guiding environmental policy. Traditional single-model approaches often struggle to accurately forecast air quality fluctuations. In response, this study introduces a robust prediction system leveraging advanced machine learning techniques. We present a comparative analysis of several models including Support Vector Regression (SVR), Genetic Algorithm-Enhanced Extreme Learning Machine (GA-KELM), and Deep Belief Network with Back-Propagation (DBN-BP). Additionally, we propose the integration of Bidirectional Long Short-Term Memory (BiLSTM), a deep learning architecture, to further enhance prediction accuracy. Through comprehensive experimentation and evaluation, we demonstrate that BiLSTM outperforms existing models, exhibiting lower Root Mean Square Error (RMSE) and Mean Squared Error (MSE) values. Furthermore, by incorporating GA-KELM, we optimize the performance of BiLSTM, enhancing its predictive capabilities even further. The proposed hybrid model not only offers improved accuracy in air quality forecasting but also contributes to informed decision-making for pollution control strategies and public health interventions. This research underscores the significance of exploring innovative techniques to address pressing environmental challenges and underscores the potential of machine learning in advancing air quality management.