Refine your search�ソスF     
Report No.

CityTransformer; A Transformer-based model for contaminant dispersion prediction in a realistic urban area

Asahi, Yuichi   ; Onodera, Naoyuki   ; Hasegawa, Yuta   ; Shimokawabe, Takashi*; Shiba, Hayato*; Idomura, Yasuhiro   

We develop a Transformer-based deep learning model to predict the plume concentrations in the urban area under uniform flow conditions. Our model has two distinct input layers: Transformer layers for sequential data and convolutional layers in convolutional neural networks (CNNs) for image-like data. Our model can predict the plume concentration from realistically available data such as the time series monitoring data at a few observation stations and the building shapes and the source location. It is shown that the model can give reasonably accurate prediction with orders of magnitude faster than CFD simulations. It is also shown that the exactly same model can be applied to predict the source location, which also gives reasonable prediction accuracy.



- Accesses




Category:Meteorology & Atmospheric Sciences



[CLARIVATE ANALYTICS], [WEB OF SCIENCE], [HIGHLY CITED PAPER & CUP LOGO] and [HOT PAPER & FIRE LOGO] are trademarks of Clarivate Analytics, and/or its affiliated company or companies, and used herein by permission and/or license.