Future Air Quality Prediction Using Long Short-Term Memory Based on Hyper Heuristic Multi-Chain Model

Air pollution is a critical global concern, demanding precise air quality forecasting to mitigate its severe consequences. Our study introduces Future Air Quality Prediction using Long Short-Term Memory based on Hyper Heuristic Multi-Chain Model (H2MCM) to project future air quality, considering various meteorological factors (MFs) and pollution-related variables like atmospheric pressure, temperature, humidity, and wind patterns. Leveraging 12 units of Long Short-Term Memory neural networks (LSTMs), H2MCM accurately predicts forthcoming air pollutants (APs) concentrations such as particulate matter with diameter 2.5 µm (PM2.5), carbon monoxide (CO), and nitrogen dioxide (NO2). Additionally, it accounts for spatiotemporal correlations between these APs and MFs, which significantly influence the air quality prediction for the next immediate time interval. H2MCM utilizes a multi-chain mechanism, employing 1-hour prediction models to forecast air quality hourly, enabling approximations for the next 12 hours. Also, for an efficient model selection, Akaike Information Criterion (AIC), Schwarz Bayesian Information Criterion (SBIC), Hannan-Quinn Information Criterion (HQIC), and corrected AIC (AICc) tools are used based on their ability to balance model fit and complexity. Furthermore, it demonstrates the ability to enhance the performance of any predictor. Experimental results substantiate H2MCM’s superiority over various models, including the Support Vector Regressor (SVR), Multi-Layer Perceptron (MLP), Recurrent Air Quality Predictor (RAQP), and Valchogianni models. H2MCM achieves impressive up to 75% better accuracy and consistency compared to SVR, 60% better than MLP, 38% better than RAQP, and 70% better than Valchogianni models. This research introduces a hybrid deep learning model for precise air quality prediction, crucial for effective environmental monitoring. The model integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTMs) to capture both spatial and temporal dependencies within air pollution data, utilizing a comprehensive dataset from Kaggle containing PM2.5, CO, NO2, and SO2 measurements. Initially, data preprocessing addresses missing values and normalizes features. Subsequently, a CNN extracts spatial patterns, identifying relationships between pollutants, while an LSTM analyzes temporal sequences, capturing air quality evolution.