[论文解读] Deep Air Quality Forecasting Using Hybrid Deep Learning Framework

Shengdong Du, Tianrui Li+2 more|arXiv (Cornell University)|Dec 12, 2018

Air Quality Monitoring and Forecasting参考文献 35被引用 47

一句话总结

本文提出一种混合深度学习模型，将 1D CNNs 和 Bi-LSTM 网络结合，以通过学习多变量空气质量时间序列中的局部时空特征和相互依赖性来预测 PM2.5。

ABSTRACT

Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this paper, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality related time series data by hybrid deep learning architecture. Due to the nonlinear and dynamic characteristics of multivariate air quality time series data, the base modules of our model include one-dimensional Convolutional Neural Networks (1D-CNNs) and Bi-directional Long Short-term Memory networks (Bi-LSTM). The former is to extract the local trend features and spatial correlation features, and the latter is to learn spatial-temporal dependencies. Then we design a jointly hybrid deep learning framework based on one-dimensional CNNs and Bi-LSTM for shared representation features learning of multivariate air quality related time series data. We conduct extensive experimental evaluations using two real-world datasets, and the results show that our model is capable of dealing with PM2.5 air pollution forecasting with satisfied accuracy.

研究动机与目标

• 推动对空气质量（PM2.5）进行准确预测，以用于早期预警和控制。
• 开发一个能够捕捉多变量空气质量时间序列中的时空相关性的模型。
• 将 1D CNNs 和 Bi-LSTM 集成到一个统一框架中，以实现共享表示学习。

提出的方法

• 使用一维 CNNs 提取局部趋势特征和空间相关性。
• 使用 Bi-directional LSTM 网络来学习时空依赖。
• 设计一个联合的混合框架，将 CNN 和 Bi-LSTM 结合，用于多变量时间序列的共享表示。
• 在两个具有 PM2.5 预测任务的真实世界数据集上评估该模型。

实验结果

研究问题

• RQ1 混合 CNN-Bi-LSTM 框架是否能够有效捕捉多变量空气质量数据中的局部和全局时空相关性？
• RQ2 与基线模型相比，所提出的体系结构是否在真实数据集上提高 PM2.5 预测准确性？

主要发现

• 该模型在真实世界数据上的 PM2.5 预测表现出令人满意的准确性。
• 该架构同时学习局部趋势特征和多变量空气质量时间序列之间的相互依赖关系。
• 实验评估表明该混合框架在空气质量预测方面的有效性。