用深度学习预测两地雾霾污染物浓度，效果优于传统模型。
Forecasting Smog Clouds With Deep Learning
- 构建分层GRU模型，融合大气科学先验知识与多任务学习。
- 在两地气象数据下，对NO2、O3、PM10和PM2.5实现高精度长期预测。
- 模型结构清晰，适合环境监测与空气质量预警场景。
本概念验证研究中，我们利用多种深度学习模型，基于两个地点的气象协变量，对氮氧化物（NO2）、臭氧（O3）及细颗粒物（PM10与PM2.5）浓度进行多变量时间序列预测，重点考察长短期记忆（LSTM）与门控循环单元（GRU）架构。特别地，我们提出一种受空气污染动态与大气科学启发的集成式分层模型架构，采用多任务学习，并与单向及全连接模型进行对比。结果表明，分层GRU在预测雾霾相关污染物浓度方面表现优异，具备竞争力且高效。
原文摘要 · Abstract (English)
In this proof-of-concept study, we conduct multivariate timeseries forecasting for the concentrations of nitrogen dioxide (NO2), ozone (O3), and (fine) particulate matter (PM10 & PM2.5) with meteorological covariates between two locations using various deep learning models, with a focus on long short-term memory (LSTM) and gated recurrent unit (GRU) architectures. In particular, we propose an integrated, hierarchical model architecture inspired by air pollution dynamics and atmospheric science that employs multi-task learning and is benchmarked by unidirectional and fully-connected models. Results demonstrate that, above all, the hierarchical GRU proves itself as a competitive and efficient method for forecasting the concentration of smog-related pollutants.
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