Hybrid VMD-CNN1D Framework: Evaluating Decomposition’s Contribution to PM2.5 Prediction
Findings show that rigorous parameter selection, bias correction, and multi-run validation, not architectural complexity alone, make decomposition-based deep learning reliable for PM2.5 prediction, offering a reproducibility-aware baseline for a future Jakarta air-quality early-warning system.