Graph Attention Sensor Transformer for Industrial Emission Forecasting: A Comparative Study Against Classical and Deep Learning Baselines
Published in Mathematical Geosciences, 2026
This paper introduces a Graph Attention Sensor Transformer that models inter-sensor spatial dependencies and temporal dynamics for industrial emission forecasting, benchmarked against classical statistical and deep learning baselines.
Recommended citation: Chang-Silva, R., Song, N., Lee, K., & Park, S. (2026). "Graph Attention Sensor Transformer for Industrial Emission Forecasting: A Comparative Study Against Classical and Deep Learning Baselines." Mathematical Geosciences. https://doi.org/10.1007/s11004-026-10320-x
