The Application of Artificial Intelligence on the Traceability and Dispersion of Air Pollution

With the global warming and global industrialization, the issue of air pollution emissions, diffusion and traceability has become a research hotspot for scientists. Traditional pollutant traceability and diffusion modeling methods are mostly limited only to the physical modeling, and cannot deal with real and complicated air pollution problems full of random conditions. This work explores the application of artificial intelligence technology in the field of pollutant traceability and diffusion modeling, using K-Means++ and SGD (stochastic gradient decent) to replace physical modeling methods, which greatly reduced the interference caused by random conditions in real situations. The results show that the pollutant emission source location and pollutant diffusion model obtained by this traceability method can well reproduce the observed pollutant distribution of air pollution, which shows that artificial intelligence technology has great prospect in the field of resolving air pollution traceability and pollutant diffusion modeling, and can be applied to the traceability of various fluid diffusion processes and pollutant modeling research.