Air Quality Prediction of Three Provinces in Central China Based on Hybrid K-Means-LSTM

DOI

10.2991/978-94-6463-056-5_19How to use a DOI?

Keywords

Air quality prediction; Data clustering; K-Means-LSTM; Central China

Abstract

With the development of industrialization and urbanization, air pollution is becoming more and more serious. To protect people's health, reasonably predict air quality and provide suggestions for people's egress, this article constructs a short-term air quality prediction model based on K-Means-LSTM. The results show that the daily meteorological data of three provinces in Central China, Henan Province, Hubei Province and Hunan Province from 2017 to 2019 are selected, the daily average AQI is taken as the target variable, and the provincial capital city of each province from October to December 2019 is selected as the test data. The prediction accuracy of K-Means-LSTM model is better than LSTM, BPNN and XGBoost, indicating the practicability of the model proposed in this research.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Guoqu Deng
AU  - Hu Chen
AU  - Siqi Wang
PY  - 2022
DA  - 2022/12/29
TI  - Air Quality Prediction of Three Provinces in Central China Based on Hybrid K-Means-LSTM
BT  - Proceedings of the 2022 2nd International Conference on Management Science and Software Engineering (ICMSSE 2022)
PB  - Atlantis Press
SP  - 135
EP  - 142
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-056-5_19
DO  - 10.2991/978-94-6463-056-5_19
ID  - Deng2022
ER  -