K-means aided spatio-temporal CNN-LSTM network for air quality forecasting

Zaifeng Zhang; Ailan Xu; Honghui Ji

doi:10.1117/12.3026542

9 February 2024 K-means aided spatio-temporal CNN-LSTM network for air quality forecasting

Zaifeng Zhang, Ailan Xu, Honghui Ji

Proceedings Volume 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023); 130730C (2024) https://doi.org/10.1117/12.3026542
Event: Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 2023, Changsha, China

Abstract

Forecasting air quality is a crucial technical approach to effectively respond to severe pollution conditions. The evolution of pollutant concentration has spatial correlation Due to the challenge of identifying monitoring stations with significant spatial correlation, a method utilizing the K-means clustering algorithm is proposed for partitioning air quality monitoring stations. Taking Nantong as an example, based on the selection of historical pollutant data in the target area, combined with meteorological data, the hybrid CNN-LSTM model, which consists of the convolutional neural network(CNN) and the long short-term memory(LSTM) neural network, is used to predict the pollutants, and finally realize the extraction of the temporal and spatial evolution characteristics of the pollutant concentration to complete the high accuracy of air quality forecast. Experimental results show that, after adding historical pollutant concentration data from stations in the cluster, the CNN-LSTM model can forecast PM2.5 concentrations precisely.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zaifeng Zhang, Ailan Xu, and Honghui Ji "K-means aided spatio-temporal CNN-LSTM network for air quality forecasting", Proc. SPIE 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 130730C (9 February 2024); https://doi.org/10.1117/12.3026542

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Atmospheric modeling

Air quality

Environmental monitoring

Meteorology

Education and training

Performance modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years