Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models

Thanongsak Xayasouk,Hwamin Lee,Giyeol Lee

doi:10.3390/su12062570

Thanongsak Xayasouk, Hwamin Lee + Show 1 more

Open Access

https://doi.org/10.3390/su12062570

Copy DOI

Abstract

Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM10 and PM2.5), which has led to concerns about human health impacts in urban areas. In this study, we developed models to predict fine PM concentrations using long short-term memory (LSTM) and deep autoencoder (DAE) methods, and compared the model results in terms of root mean square error (RMSE). We applied the models to hourly air quality data from 25 stations in Seoul, South Korea, for the period from 1 January 2015, to 31 December 2018. Fine PM concentrations were predicted for the 10 days following this period, at an optimal learning rate of 0.01 for 100 epochs with batch sizes of 32 for LSTM model, and DAEs model performed best with batch size 64. The proposed models effectively predicted fine PM concentrations, with the LSTM model showing slightly better performance. With our forecasting model, it is possible to give reliable fine dust prediction information for the area where the user is located.

Highlights

As industry and population expand rapidly in South Korea, air pollution is increasingly becoming problematic for human health in the country
Air pollution in urban areas consists of carbon dioxide (CO2 ), carbon monoxide (CO), nitrogen oxide (NO2 ), nitrogen monoxide (NO), ozone (O3 ), and fine particulate matter (PM), the last of which is of greatest concern in South Korea
We split the data into training and test datasets and applied the long short-term memory (LSTM) and deep autoencoder (DAE) models to predict PM10 and PM2.5 concentrations for the 10 days following the study period

Summary

Introduction

As industry and population expand rapidly in South Korea, air pollution is increasingly becoming problematic for human health in the country. In 2017, South Korea ranked 173rd among the 180 countries with the greatest air pollution impact [1]. Air pollution in urban areas consists of carbon dioxide (CO2 ), carbon monoxide (CO), nitrogen oxide (NO2 ), nitrogen monoxide (NO), ozone (O3 ), and fine particulate matter (PM), the last of which is of greatest concern in South Korea. Fine PM is classified into PM10 and PM2.5 based on particle diameter, where PM10 and PM2.5 are particles with diameters

Methods

Results

Conclusion