Particle Swarm Optimization based hybrid Deep Learning approach: Evidence from Europe for Predicting Air Quality Index
DOI:
https://doi.org/10.53799/5gm39s77Keywords:
Air pollution, AQI, PSO+FNN+LR, PSO+FNN, wind rose, SHAPAbstract
Air pollution remains a worldwide concern, and traditional machine-learning models often struggle to capture the complex, nonlinear relationships between the Air Quality Index (AQI) and its influencing variables. This study proposes two Particle Swarm Optimization (PSO) - based hybrid deep-learning approaches for predicting AQI by incorporating the necessary variables. In the current study, one approach uses PSO, deep learning, and Linear Regression (LR) for feature selection, feature extraction, and AQI prediction, respectively, while the other employs PSO to optimize hyperparameters of deep learning. The study examines wind speed and direction using wind rose diagrams and evaluates the features' contributions using the Shapley Additive exPlanations (SHAP) analysis. Using the Wilcoxon signed-rank test, statistical validation shows that both approaches outperform existing methods at 34 monitoring locations. Specifically, the first approach is effective for 19 locations, with an average R² of 97.0%, while the second approach performs best for 33 locations, with an average R² of 98.17%. Our findings recommend implementing region-specific air quality policies across Europe. It will aid policymakers in designing targeted, effective, and data-driven interventions to improve public health and environmental resilience across Europe.
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