Deep Air Quality Forecasting Using Hybrid Deep Learning Framework

TL;DR

This paper introduces a hybrid deep learning model combining 1D-CNNs and Bi-LSTM to improve the accuracy of PM2.5 air quality forecasting by capturing spatial-temporal dependencies in multivariate data.

Contribution

The paper presents a novel hybrid deep learning framework that effectively learns spatial-temporal features for air quality prediction, outperforming existing methods.

Findings

Model achieves high accuracy in PM2.5 forecasting

Effective learning of spatial-temporal dependencies

Validated on two real-world datasets

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.