Deep Random Subspace Learning: A Spatial-Temporal Modeling Approach for Air Quality Prediction

Xiaotong Sun,Wei Xu

doi:10.3390/atmos10090560

Abstract

Decrease in air quality is one of the most crucial threats to human health. There is an imperative and necessary need for more accurate air quality prediction. To meet this need, we propose a novel long short-term memory-based deep random subspace learning (LSTM-DRSL) framework for air quality forecasting. Specifically, we incorporate real-time pollutant emission data into the model input. We also design a spatial-temporal analysis approach to make good use of these data. The prediction model is developed by combining random subspace learning with a deep learning algorithm in order to improve the prediction accuracy. Empirical analyses based on multiple datasets over China from January 2015 to September 2017 are performed to demonstrate the efficacy of the proposed framework for hourly pollutant concentration prediction at an urban-agglomeration scale. The empirical results indicate that our framework is a viable method for air quality prediction. With consideration of the regional scale, the LSTM-DRSL framework performs better at a relatively large regional scale (around 200–300 km). In addition, the quality of predictions is higher in industrial areas. From a temporal point of view, the LSTM-DRSL framework is more suitable for hourly predictions.

Highlights

The pervasiveness of poor air quality in both developing and developed countries has brought about a global threat, having huge negative impacts on the environment and health
We think that the incorporation of pollutant emission information on heating plants is a possible reason for our framework to perform best in winter and worst in summer
This study focused on air pollution predictions and proposed a possible reason for our framework to perform best in winter and worst in summer

Summary

Introduction

The pervasiveness of poor air quality in both developing and developed countries has brought about a global threat, having huge negative impacts on the environment and health. Many studies across the world seek ways to quantify the magnitude of health harm caused by air pollution through systematic scientific efforts (e.g., [2]). Health Organization (WHO) data, about 4.2 million deaths happen every year due to ambient air pollution. Premature diseases such as lung disease, heart disease, and stroke, etc., have been noted as being mainly caused by air pollutants. To protect people from these adverse health impacts, people are encouraged by the American Lung Association to pay more attention to air quality forecasts and take timely precautions [6]. Air quality forecasts making predictions of air pollutant concentrations have become imperative and urgent necessities for air quality control. An O3 action plan employed in French Bouches du Rhône uses air quality forecasting as a tool to trigger an emission reduction strategies selection process [7]

Methods

Results

Conclusion