Wu Yong, Wang Xiaochu, Wang Meizhen, Liu Xuejun, Zhu Sifeng
School of Geographical Sciences, Fujian Normal University, Fuzhou 350117, China.
Shanghai Surveying and Mapping Institute, Shanghai 200063, China.
Sensors (Basel). 2024 Dec 27;25(1):95. doi: 10.3390/s25010095.
Accurate and timely air quality forecasting is crucial for mitigating pollution-related hazards and protecting public health. Recently, there has been a growing interest in integrating visual data for air quality prediction. However, some limitations remain in existing literature, such as their focus on coarse-grained classification, single-moment estimation, or reliance on indirect and unintuitive information from visual images. Here we present a dual-channel deep learning model, integrating surveillance images and multi-source numerical data for air quality forecasting. Our model, which combines a single-channel hybrid network consisting of VGG16 and LSTM (named VGG16-LSTM) with a single-channel Long Short-Term Memory (LSTM) network, efficiently captures detailed spatiotemporal features from surveillance image sequences and temporal features from atmospheric, meteorological, and temporal data, enabling accurate time-series forecasting of PM and PM concentrations. Experiments conducted on the 2021 Shanghai dataset demonstrate that the proposed model significantly outperforms traditional machine learning methods in terms of accuracy and robustness for time-series forecasting, achieving values of 0.9459 and 0.9045 and RMSE values of 4.79 μg/m and 11.51 μg/m for PM and PM, respectively. Furthermore, validation results on the datasets from two stations in Kaohsiung, Taiwan, with average values of 0.9728 and 0.9365 and average RMSE values of 1.89 μg/m and 5.69 μg/m for PM and PM using a pretrain-finetune training strategy, confirm the model's adaptability across diverse geographical contexts. These findings highlight the potential of integrating surveillance images to enhance air quality prediction, offering an effective supplement to ground-level environmental monitoring. Future work will focus on expanding datasets and optimizing network architectures to further improve forecasting accuracy and computational efficiency, enhancing the model's scalability for broader regional air quality management.
准确及时的空气质量预测对于减轻污染相关危害和保护公众健康至关重要。最近，将视觉数据集成到空气质量预测中的兴趣日益浓厚。然而，现有文献仍存在一些局限性，例如它们侧重于粗粒度分类、单时刻估计，或依赖于视觉图像中的间接且不直观的信息。在此，我们提出一种双通道深度学习模型，将监控图像和多源数值数据集成用于空气质量预测。我们的模型将由VGG16和LSTM组成的单通道混合网络（名为VGG16-LSTM）与单通道长短期记忆（LSTM）网络相结合，能够有效地从监控图像序列中捕捉详细的时空特征，并从大气、气象和时间数据中捕捉时间特征，从而实现对PM和PM浓度的准确时间序列预测。在2021年上海数据集上进行的实验表明，所提出的模型在时间序列预测的准确性和稳健性方面显著优于传统机器学习方法，PM和PM的 值分别达到0.9459和0.9045，RMSE值分别为4.79μg/m和11.51μg/m。此外，使用预训练-微调训练策略在台湾高雄两个站点的数据集上进行验证，PM和PM的平均 值分别为0.9728和0.9365，平均RMSE值分别为1.89μg/m和5.69μg/m，证实了该模型在不同地理环境中的适应性。这些发现凸显了集成监控图像以增强空气质量预测的潜力，为地面环境监测提供了有效的补充。未来的工作将集中在扩展数据集和优化网络架构，以进一步提高预测准确性和计算效率，增强模型在更广泛区域空气质量管理中的可扩展性。
