Predicting PM2.5 in the Northeast China Heavy Industrial Zone: A Semi-Supervised Learning with Spatiotemporal Features
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{jiang:2022:Atmosphere,
-
author = "Hongxun Jiang and Xiaotong Wang and Caihong Sun",
-
title = "Predicting {PM2.5} in the Northeast China Heavy
Industrial Zone: A Semi-Supervised Learning with
Spatiotemporal Features",
-
journal = "Atmosphere",
-
year = "2022",
-
volume = "13",
-
number = "11",
-
pages = "Article No. 1744",
-
keywords = "genetic algorithms, genetic programming",
-
ISSN = "2073-4433",
-
URL = "https://www.mdpi.com/2073-4433/13/11/1744",
-
DOI = "doi:10.3390/atmos13111744",
-
abstract = "Particulate matter PM2.5 pollution affects the Chinese
population, particularly in cities such as Shenyang in
northeastern China, which occupies a number of
traditional heavy industries. This paper proposes a
semi-supervised learning model used for predicting
PM2.5 concentrations. The model incorporates rich data
from the real world, including 11 air quality
monitoring stations in Shenyang and nearby cities.
There are three types of data: air monitoring,
meteorological data, and spatiotemporal information
(such as the spatiotemporal effects of PM2.5 emissions
and diffusion across different geographical regions).
The model consists of two classifiers: genetic
programming (GP) to forecast PM2.5 concentrations and
support vector classification (SVC) to predict trends.
The experimental results show that the proposed model
performs better than baseline models in accuracy,
including 3percent to 18percent over a classic
multivariate linear regression (MLR), 1percent to
11percent over a multi-layer perceptron neural network
(MLP-ANN), and 21percent to 68percent over a support
vector regression (SVR). Furthermore, the proposed GP
approach provides an intuitive contribution analysis of
factors for PM2.5 concentrations. The data of
backtracking points adjacent to other monitoring
stations are critical in forecasting shorter time
intervals (1 h). Wind speeds are more important in
longer intervals (6 and 24 h).",
-
notes = "also known as \cite{atmos13111744}",
- }
Genetic Programming entries for
Hongxun Jiang
Xiaotong Wang
Caihong Sun
Citations