一种模型驱动与数据驱动的空气质量精确预报融合框架
A Model-driven and Data-driven Fusion Framework for Accurate Air Quality Prediction
摘要：
空气质量与公众健康密切相关。心血管疾病和呼吸道疾病等健康问题可能与长期暴露于高度污染的环境有关。因此，准确的空气质量预报对脆弱人群至关重要。为了估计几种空气污染浓度的变化，以前的研究人员使用了各种方法，如社区多尺度空气质量模型（CMAQ）或神经网络。虽然CMAQ模型考虑了历史空气污染数据和气象变量的覆盖范围，但由于额外的调整，引入了额外的偏差。本文提出了一种基于模型的策略和数据驱动的方法相结合的物理-时态收集（PTC）模型，旨在解决传统模型带来的系统误差。在数据驱动部分，第一个组成部分是时间模式和天气模式，以衡量有助于预测性能的重要特征。将删除不太相关的输入变量，以消除网络训练中的负权重。然后，我们部署了一个长短期记忆（LSTM）来获取初步结果，这将进一步校正由一个神经网络（NN）涉及气象指数以及其他污染物浓度。我们应用于预测的数据集为2016年1月1日至2016年12月31日。实验结果表明，与基准模型（CMAQ预测、GRU、DNN等）相比，本文提出的PTC模型具有很好的性能。这种基于联合模型的数据驱动的空气质量预测方法可以很容易地部署在站点上，而无需额外的调整，为脆弱的成员提供高时间分辨率的信息，以预防未来的严重空气污染。
英文摘要：
Air quality is closely related to public health. Health issues such as cardiovascular diseases and respiratory diseases, may have connection with long exposure to highly polluted environment. Therefore, accurate air quality forecasts are extremely important to those who are vulnerable. To estimate the variation of several air pollution concentrations, previous researchers used various approaches, such as the Community Multiscale Air Quality model (CMAQ) or neural networks. Although CMAQ model considers a coverage of the historic air pollution data and meteorological variables, extra bias is introduced due to additional adjustment. In this paper, a combination of model-based strategy and data-driven method namely the physical-temporal collection(PTC) model is proposed, aiming to fix the systematic error that traditional models deliver. In the data-driven part, the first components are the temporal pattern and the weather pattern to measure important features that contribute to the prediction performance. The less relevant input variables will be removed to eliminate negative weights in network training. Then, we deploy a long-short-term-memory (LSTM) to fetch the preliminary results, which will be further corrected by a neural network (NN) involving the meteorological index as well as other pollutants concentrations. The data-set we applied for forecasting is from January 1st, 2016 to December 31st, 2016. According to the results, our PTC achieves an excellent performance compared with the baseline model (CMAQ prediction, GRU, DNN and etc.). This joint model-based data-driven method for air quality prediction can be easily deployed on stations without extra adjustment, providing results with high-time-resolution information for vulnerable members to prevent heavy air pollution ahead.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
