Prediction of PM_2.5 concentration based on a CNN-LSTM neural network algorithm.

ABSTRACT: Fine particulate matter (PM_2.5) is a major air pollutant affecting human survival, development and health. By predicting the spatial distribution concentration of PM_2.5, pollutant sources can be better traced, allowing measures to protect human health to be implemented. Thus, the purpose of this study is to predict and analyze the PM_2.5 concentration of stations based on the integrated deep learning of a convolutional neural network long short-term memory (CNN-LSTM) model. To solve the complexity and nonlinear characteristics of PM_2.5 time series data problems, we adopted the CNN-LSTM deep learning model. We collected the PM_2.5data of Qingdao in 2020 as well as meteorological factors such as temperature, wind speed and air pressure for

SUBMITTER: Bai X 

PROVIDER: S-EPMC11313410 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature