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Development of a deep learning model for predicting plume concentrations in the urban area

Asahi, Yuichi   ; Onodera, Naoyuki   ; Hasegawa, Yuta   ; Idomura, Yasuhiro   

We have developed a convolutional neural network (CNN) model to predict the plume concentrations in the urban area under uniform flow condition. By combining the Transformer or Multilayer Perceptron (MLP) layers with CNN model, our model can predict the plume concentrations from the building shapes, release points of plume and time series data at observation stations. It is also shown that the exactly same model can be applied to predict the source location, which also gives reasonable prediction accuracy.

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