Improving PM 2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm.

Autor: Masood A; Department of Civil Engineering, Jamia Millia Islamia University, New Delhi, India., Hameed MM; Department of Civil Engineering, Al-Maarif University College, Ramadi, Iraq. mohmmag1@gmail.com., Srivastava A; Department of Civil Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, 721302, West Bengal, India., Pham QB; Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska Street 60, 41-200, Sosnowiec, Poland., Ahmad K; Department of Civil Engineering, Jamia Millia Islamia University, New Delhi, India., Razali SFM; Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia.; Smart and Sustainable Township Research Centre (SUTRA), Universiti Kebangsaan Malaysia (UKM), 43600, UKM Bangi, Selangor, Malaysia.; Green Engineering and Net Zero Solution (GREENZ), Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia., Baowidan SA; Information Technology Department Faculty of Computing and IT, King Abdulaziz University, Jeddah, Saudi Arabia.; Center of Excellence in Environmental Studies, King Abdulaziz University, Jeddah, Saudi Arabia.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Nov 29; Vol. 13 (1), pp. 21057. Date of Electronic Publication: 2023 Nov 29.
DOI: 10.1038/s41598-023-47492-z
Abstrakt: Fine particulate matter (PM 2.5 ) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM 2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM 2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM 2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R 2 ) of 0.928, and root mean square error of 30.325 µg/m 3 . The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM 2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.
(© 2023. The Author(s).)
Databáze: MEDLINE
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