Identifying a suitable model for predicting hourly pollutant concentrations by using low-cost microstation data and machine learning.

ABSTRACT: Accurately predicting the concentration of PM_2.5 (fine particles with a diameter of 2.5 μm or less) is essential for health risk assessment and formulation of air pollution control strategies. At present, there is also a large amount of air pollution data. How to efficiently mine its hidden features to obtain the future concentration of pollutants is very important for the prevention and control of air pollution. Therefore we build a pollutant prediction model based on Lightweight Gradient Boosting Model (LightGBM) shallow machine learning and Long Short-Term Memory (LSTM) neural network. Firstly, the PM_2.5 pollutant concentration data of 34 air quality stations in Beijing and the data of 18 weather stations were matched in time and space to obtain an input data set.

SUBMITTER: Yang R 

PROVIDER: S-EPMC9675857 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature