Researchers publish PM2.5 forecasting model using VMD and deep learning

The manuscript "Forecasting of PM2.5 concentration based on variational mode decomposition and deep learning," by Yun Cheng and Chao Zhang, was published as a preprint in Scientific Reports on 05 June 2026. Per the manuscript, the authors apply **variational mode decomposition (VMD)** to split PM2.5 time series into intrinsic mode functions, compute sample entropy to measure complexity, and use K-means to cluster components into high-, medium-, and low-frequency bands. They train separate `TCN`-`BiLSTM` forecasting models per band and apply an attention-based weighted fusion to produce final predictions. According to the paper, the approach achieved a lowest RMSE of **16.920 µg/m3**, a lowest MAE of **11.134 µg/m3**, and a highest R² of **0.960** on their evaluation dataset (Scientific Reports, 05 June 2026). Editorial analysis: the paper exemplifies a common hybrid signal-decomposition plus deep-learning pattern for environmental forecasting and will be of practical interest to practitioners building short-term air-quality predictors. The manuscript "Forecasting of PM2.5 concentration based on variational mode decomposition and deep learning," by Yun Cheng and Chao Zhang, was published as a preprint in Scientific Reports on 05 June 2026. Per the manuscript, the authors apply varia... [1653 chars]