PM2.5 concentration prediction in Lanzhou, China, using hyperchaotic cuckoo search-extreme learning machine

摘要

High concentrations of PM2.5 cause environmental problems and many serious health effects, including heart and lung disease. To assess the PM2.5 level with high accuracy, an advanced extreme learning machine (ELM) model is proposed, which combines the advantages of the hyperchaotic system and the cuckoo search algorithm. It improves the accuracy of the original ELM and avoids manual adjustment of parameters. The model examines the PM2.5 concentration in Lanzhou, China, with daily predictions at four stations in 2018. It is also compared with different methods, such as the original ELM, multiple linear regression, and long short-term memory, and the results show that the proposed model obtains better forecasting performance than the others in terms of root mean squared error and the coefficient of determination (R-2), respectively. In addition, the model is applied to make short-term forecasts for four stations, predicting hourly PM2.5 concentrations over the next week based on fourteen days of monitoring data. They are in high agreement with the monitored PM2.5 concentrations. Our research indicates that the proposed model can facilitate effective measures to avoid exposure to high concentrations of PM2.5. Meanwhile, it also provides a novel way to predict air pollution.