Prediction of daily PM2.5 concentration in China using data-driven ordinary differential equations
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- @Article{WANG:2020:AMC,
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author = "Yufang Wang and Haiyan Wang and Shuhua Zhang",
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title = "Prediction of daily {PM2.5} concentration in China
using data-driven ordinary differential equations",
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journal = "Applied Mathematics and Computation",
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volume = "375",
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pages = "125088",
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year = "2020",
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ISSN = "0096-3003",
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DOI = "doi:10.1016/j.amc.2020.125088",
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URL = "http://www.sciencedirect.com/science/article/pii/S0096300320300576",
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keywords = "genetic algorithms, genetic programming, Concentration
data, Least square method, ODE models, prediction",
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abstract = "Accurate reporting and forecasting of PM2.5
concentration are important for improving public
health. In this paper, we propose a daily prediction
method of PM2.5 concentration by using data-driven
ordinary differential equation (ODE) models.
Specifically, based on the historical PM2.5
concentration, this method combines genetic programming
and orthogonal least square method to evolve the ODE
models, which describe the transport of PM2.5 and then
uses the data-driven ODEs to predict the air quality in
the future. Experiment results show that the ODE models
obtain similar prediction results as the typical
statistical model, and the prediction results from this
method are relatively good. To our knowledge, this is
the first attempt to evolve data-driven ODE models to
study PM2.5 prediction",
- }
Genetic Programming entries for
Yufang Wang
Haiyan Wang
Shuhua Zhang
Citations