A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Flow velocity prediction method based on deep learning
The prediction of port channel flow velocity is very important for offshore operation, navigation safety and coastal engineering construction. This paper focuses on the prediction of short-term flow velocity in port waters. By considering the characteristics of time dependence and feature dependence in flow velocity prediction, a hybrid model of CNN, Bi _ LSTM and self-attention mechanism is integrated, and a SA-CNN-Bi _ LSTM model is proposed to improve the performance of traditional models in flow velocity prediction. The port flow velocity prediction model based on deep neural network established in this paper can take advantage of CNN and Bi _ LSTM to extract features
Flow velocity prediction method based on deep learning
The prediction of port channel flow velocity is very important for offshore operation, navigation safety and coastal engineering construction. This paper focuses on the prediction of short-term flow velocity in port waters. By considering the characteristics of time dependence and feature dependence in flow velocity prediction, a hybrid model of CNN, Bi _ LSTM and self-attention mechanism is integrated, and a SA-CNN-Bi _ LSTM model is proposed to improve the performance of traditional models in flow velocity prediction. The port flow velocity prediction model based on deep neural network established in this paper can take advantage of CNN and Bi _ LSTM to extract features
Flow velocity prediction method based on deep learning
Ghanizadeh, Ali Reza (editor) / Jia, Hongfei (editor) / Zhao, Zihan (author) / Yang, Zihao (author)
Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023) ; 2023 ; Dalian, China
Proc. SPIE ; 13064
2024-02-20
Conference paper
Electronic Resource
English
A deep learning traffic flow prediction framework based on multi-channel graph convolution
| Taylor & Francis Verlag | 2021