A Machine Learning-Based Ensemble Framework for Forecasting PM2.5 Concentrations in Puli, Taiwan

• Published in: Applied sciences Vol. 12; no. 5; p. 2484
• Main Authors: Yin, Peng-Yeng, Yen, Alex Yaning, Chao, Shou-En, Day, Rong-Fuh, Bhanu, Bir
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2022
• Subjects: Air pollution; Anthropogenic factors; Climate change; Datasets; Deep learning; forecast; Forecasting; Industrial plant emissions; long-term learning; Machine learning; Methods; multi-model framework; Neural networks; Particulate matter; Plant species; PM2.5; Pollutants; Regression analysis; short-term learning; Stochastic models; Support vector machines; Time series; Trends; Variables; Beijing China; China; Taiwan
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12052484

Forecasting of PM2.5 concentration is a global concern. Evidence has shown that the ambient PM2.5 concentrations are harmful to human health, climate change, plant species mortality, etc. PM2.5 concentrations are caused by natural and anthropogenic activities, and it is challenging to predict them due to many uncertain factors. Current research has focused on developing a new model while overlooking the fact that every single model for PM2.5 prediction has its own strengths and weaknesses. This paper proposes an ensemble framework which combines four diverse learning models for PM2.5 forecasting in Puli, Taiwan. It explores the synergy between parametric and non-parametric learning, and short-term and long-term learning. The feature set covers periodic, meteorological, and autoregression variables which are selected by a spiral validation process. The experimental dataset, spanning from 1 January 2008 to 31 December 2019, from Puli Township in central Taiwan, is used in this study. The experimental results show the proposed multi-model framework can synergize the advantages of the embedded models and obtain an improved forecasting result. Further, the benefit obtained by blending short-term learning with long-term learning is validated, in surpassing the performance obtained by using just single type of learning. Our multi-model framework compares favorably with deep-learning models on Puli dataset. It also shows high adaptivity, such that our multi-model framework is comparable to the leading methods for PM2.5 forecasting in Delhi, India.