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01-12-2023 | Research

Research on time series change point detection and influencing factors under machine learning: based on PM2.5 concentration data in Hefei city

Authors: Maosen Xia, Linlin Dong, Lingling Jiang, Min Zeng

Published in: Earth Science Informatics | Issue 1/2024

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Abstract

To analyze the temporal variation characteristics of PM_2.5 concentration and its influencing factors in Hefei, this paper utilizes mixed frequency data of air pollution and socio-economic development from 2013 to 2021, and constructs an adaptive change point detection model and fusion of Lightgbm in Machine Learning Models and LGBNN in multilayer neural networks to examine the change point characteristics and significant factors affecting PM_2.5 concentration changes in Hefei. The findings indicate evident periodic oscillation patterns in PM_2.5 concentration in Hefei, with a greater number of decreasing variables compared to rising variables in the sequence, and the change points exhibit a distinct “phased” characteristic. Regarding the influencing factors, the feature selection analysis conducted on multiple machine learning models like LGBNN reveals that policy factors exhibit the highest prominence, followed by social economy development, air pollution, and meteorological factors.

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Title: Research on time series change point detection and influencing factors under machine learning: based on PM2.5 concentration data in Hefei city
Authors: Maosen Xia
Linlin Dong
Lingling Jiang
Min Zeng
Publication date: 01-12-2023
Publisher: Springer Berlin Heidelberg
Published in: Earth Science Informatics / Issue 1/2024
Print ISSN: 1865-0473
Electronic ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-023-01173-7

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