Hybrid deep learning model for air quality prediction and its impact on healthcare

Abstract

Air pollution poses a serious threat to public health, making accurate and timely air quality prediction essential for effective mitigation and planning. This study presents a deep learning–based framework for short-term Air Quality Index (AQI) forecasting that integrates structured feature engineering with advanced neural architectures. Engineered features include lagged pollutant indicators, multi-scale moving averages, seasonal cyclic encodings, pollutant ratios, and date-based temporal variables, designed to capture nonlinear temporal dependencies and pollutant interactions. Air quality data collected from the OpenWeather API are used to evaluate Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a hybrid CNN–LSTM architecture. Model training is performed using Adam and RMSprop optimizers, and performance is assessed using precision, recall, and F1-score. Experimental results indicate that, within the scope of the evaluated dataset, the hybrid CNN-LSTM model achieves the strongest overall performance for short-term AQI forecasting, attaining an F1-score of approximately 91%, compared with 87.9% for LSTM and 86.7% for CNN under identical configurations. The results further demonstrate that the incorporation of engineered temporal and ratio-based features consistently improves predictive performance across all models. While the study is limited to a region-specific dataset and a short time span, the findings highlight the effectiveness of combining feature engineering with hybrid deep learning architectures for robust AQI prediction and support their potential use in data-driven air quality monitoring systems.