Air Pollution Forecasting Using Temporal NNs Team VectorX

An end-to-end deep learning and machine learning framework for hourly PM2.5 air pollution forecasting across 12 Beijing monitoring stations, leveraging physics-informed atmospheric feature engineering, deep residual temporal neural networks (ResNet-1D BiLSTM), and SLSQP hybrid ensemble blending.