Journal Article

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Razali, Siti Fatin Mohd, Ahmad, Kafeel, Masood, Adil, Baowidan, Souad Ahmad, Pham, Quoc Bao, Hameed, Mohammed Majeed, Srivastava, Aman

2023

Overview

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.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 639/166 / 704/172 / Air pollution / Air pollution effects / Air quality / Algorithms / Correlation coefficient / Deep learning / Environmental health / Forecasting / Health problems / Humanities and Social Sciences / Hybridization / Learning algorithms / Long short-term memory / Machine learning / multidisciplinary / Optimization algorithms / Outdoor air quality / Particulate matter / Prediction models / Public awareness / Public health / Regression analysis / Science / Science (multidisciplinary)