计算机科学
适应性
自编码
人工神经网络
节点(物理)
人工智能
污染物
深度学习
图形
机器学习
数据挖掘
生态学
化学
结构工程
有机化学
理论计算机科学
工程类
生物
作者
Bo Zhang,Wenwen Wang,Yunjie Lu,Mao-Zhen Li,Ru Yang,Jianguo Pan,Zuliang Kou Data analysis Valiadtion
标识
DOI:10.1016/j.eswa.2023.120718
摘要
The air pollutant diffusion trend prediction plays an important role in the environment protection. The approaches in the existing studies rarely consider spatial and temporal information sequentially or cannot use spatiotemporal structure information effectively. In this paper, we propose a new deep learning-based air pollutant diffusion trend prediction model, called GATBL-Learning. It is an end-to-end neural network model consisting of Graph Attention Network (GAT) and Encoder-Decoder model. Since each city in the region will set as a node, GAT can extract spatial information from them by aggregation operation. Then Encoder-Decoder model, consisting of Bi-LSTM as Encoder and LSTM as Decoder, mines time series features and outputs the data containing spatiotemporal features. In addition, we also observe that the changes of air pollutants show seasonality discipline and training the seasonal model can improve the model adaptability. The results demonstrate the proposed prediction model is effective and superior.
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