文献类型：期刊文献

英文题名：Ada-TransGNN: An Air Quality Prediction Model Based On Adaptive Graph Convolutional Networks

作者：Wang, Dan[1]; Jiang, Feng[2]; Wang, Zhanquan[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] School of Computer Science and Technology, Changsha University of Science and Technology, Changsha, 410114, China

年份：2026

卷号：16312 LNCS

起止页码：235

外文期刊名：Lecture Notes in Computer Science

收录：EI(收录号：20254719540152)

语种：英文

外文关键词：Air quality - Benchmarking - Convolution - Data accuracy - Data mining - Distributed computer systems - Forecasting - Graph structures - Graph theory - Graphic methods - Learning systems - Prediction models

摘要：Accurate air quality prediction is becoming increasingly important in the environmental field. To address issues such as low prediction accuracy and slow real-time updates in existing models, which lead to lagging prediction results, we propose a Transformer-based spatiotemporal data prediction method (Ada-TransGNN) that integrates global spatial semantics and temporal behavior. The model constructs an efficient and collaborative spatiotemporal block set comprising a multi-head attention mechanism and a graph convolutional network to extract dynamically changing spatiotemporal dependency features from complex air quality monitoring data. Considering the interaction relationships between different monitoring points, we propose an adaptive graph structure learning module, which combines spatiotemporal dependency features in a data-driven manner to learn the optimal graph structure, thereby more accurately capturing the spatial relationships between monitoring points. Additionally, we design an auxiliary task learning module that enhances the decoding capability of temporal relationships by integrating spatial context information into the optimal graph structure representation, effectively improving the accuracy of prediction results. We conducted comprehensive evaluations on a benchmark dataset and a novel dataset (Mete-air). The results demonstrate that our model outperforms existing state-of-the-art prediction models in short-term and long-term predictions. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.